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Jigsaw Puzzles: Splitting Harmful Questions to Jailbreak Large Language Models in Multi-turn Interactions
In this work, we propose Jigsaw Puzzles (JSP), a straightforward yet effective multi-turn jailbreak strategy, exposing LLM vulnerabilities to inform future safety improvements.
Large language models (LLMs) have exhibited outstanding performance in engaging with humans and addressing complex questions by leveraging their vast implicit knowledge and robust reasoning capabilities. However, such models are vulnerable to jailbreak attacks, leading to the generation of harmful responses. Despite re...
[ "Hao Yang", "Lizhen Qu", "Ehsan Shareghi", "Gholamreza Haffari" ]
https://openreview.net/forum?id=zuNM3eoPVi
zuNM3eoPVi
zuNM3eoPVi
[ "~Hao_Yang26", "~Lizhen_Qu2", "~Ehsan_Shareghi1", "~Gholamreza_Haffari2" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/37b5d26cf61599e9f7a4d742ff910b1026aec236.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Jailbreak", "Red teaming" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
true
Does not discuss potentially harmful ramifications and dual use
@inproceedings{ yang2025jigsaw, title={Jigsaw Puzzles: Splitting Harmful Questions to Jailbreak Large Language Models in Multi-turn Interactions}, author={Hao Yang and Lizhen Qu and Ehsan Shareghi and Gholamreza Haffari}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum...
yang|jigsaw_puzzles_splitting_harmful_questions_to_jailbreak_large_language_models_in_multiturn_interactions
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null
null
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Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents
State-of-the-art results on Computer Use using a framework of Generalist and Specialist modules.
Computer use agents automate digital tasks by directly interacting with graphical user interfaces (GUIs) on computers and mobile devices, offering significant potential to enhance human productivity by completing an open-ended space of user queries. However, current agents face significant challenges: imprecise groundi...
[ "Saaket Agashe", "Kyle Wong", "Vincent Tu", "Jiachen Yang", "Ang Li", "Xin Eric Wang" ]
https://openreview.net/forum?id=zg5is4GJ3R
zg5is4GJ3R
zg5is4GJ3R
[ "~Saaket_Agashe1", "~Kyle_Wong1", "~Vincent_Tu1", "~Jiachen_Yang1", "~Ang_Li1", "~Xin_Eric_Wang2" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/51a372ef953dfccf2d22d4657f707fe1bf9383b2.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Computer Use", "GUI Agents", "Multimodal Large Language Models", "Planning", "Grounding", "Vision" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
null
null
@inproceedings{ agashe2025agent, title={Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents}, author={Saaket Agashe and Kyle Wong and Vincent Tu and Jiachen Yang and Ang Li and Xin Eric Wang}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/foru...
agashe|agent_s2_a_compositional_generalistspecialist_framework_for_computer_use_agents
/attachment/ecc357b402b416c7ea4804242770e4c521a46cfd.zip
null
null
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GenerationPrograms: Fine-grained Attribution with Executable Programs
GenerationPrograms: Fine-grained Attribution via Neural Modular Trees
Recent large language models (LLMs) achieve impressive performance in text generation but often fail to accurately attribute their outputs, undermining trust and verifiability. Moreover, existing attribution methods do not explain how and why models leverage the provided source documents to generate their final respons...
[ "David Wan", "Eran Hirsch", "Elias Stengel-Eskin", "Ido Dagan", "Mohit Bansal" ]
https://openreview.net/forum?id=zTKYKiWzIm
zTKYKiWzIm
zTKYKiWzIm
[ "~David_Wan1", "~Eran_Hirsch1", "~Elias_Stengel-Eskin1", "~Ido_Dagan1", "~Mohit_Bansal2" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/879c600a2233a7fa325b3ccd7a81cd7277332584.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "long-form qa", "rag", "summarization", "attributed text generation" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
null
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@inproceedings{ wan2025generationprograms, title={GenerationPrograms: Fine-grained Attribution with Executable Programs}, author={David Wan and Eran Hirsch and Elias Stengel-Eskin and Ido Dagan and Mohit Bansal}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=zTKY...
wan|generationprograms_finegrained_attribution_with_executable_programs
/attachment/22e800fcc6dc11a936f21b1f04a507d404c9515d.zip
null
null
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Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? A Case Study on Vaccine Hesitancy
Investigate if a multi LLM agent system can simulate human health behaviors and inform policymaking.
Can we simulate a sandbox society with generative agents to model human behavior, thereby reducing the over-reliance on real human trials for assessing public policies? In this work, we investigate the feasibility of simulating health-related decision-making, using vaccine hesitancy, defined as the delay in acceptance ...
[ "Abe Bohan Hou", "Hongru Du", "Yichen Wang", "Jingyu Zhang", "Zixiao Wang", "Paul Pu Liang", "Daniel Khashabi", "Lauren M Gardner", "Tianxing He" ]
https://openreview.net/forum?id=zSbecER9il
zSbecER9il
zSbecER9il
[ "~Abe_Bohan_Hou1", "~Hongru_Du1", "~Yichen_Wang4", "~Jingyu_Zhang2", "~Zixiao_Wang6", "~Paul_Pu_Liang1", "~Daniel_Khashabi2", "~Lauren_M_Gardner1", "~Tianxing_He1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/efdbcf1c87e1f5c9d8ded3b5bd028477a557f88e.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "LLM agent", "multi-agent system", "social simulation", "public health", "AI for health" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ hou2025can, title={Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? A Case Study on Vaccine Hesitancy}, author={Abe Bohan Hou and Hongru Du and Yichen Wang and Jingyu Zhang and Zixiao Wang and Paul Pu Liang and Daniel Khashabi and Lauren M Gardner and Tianxing ...
hou|can_a_society_of_generative_agents_simulate_human_behavior_and_inform_public_health_policy_a_case_study_on_vaccine_hesitancy
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REFA: Reference Free Alignment with Fine-Grained Length Control
Reference-free alignment methods that optimize over multiple user preferences with fine-grained control of length
To mitigate reward hacking from response verbosity, modern preference optimization methods are increasingly adopting length normalization (e.g., SimPO, ORPO, LN-DPO). While effective against this bias, we demonstrate that length normalization itself introduces a failure mode: the **URSLA shortcut**. Here models learn t...
[ "Taneesh Gupta", "Rahul Madhavan", "Xuchao Zhang", "Chetan Bansal", "Saravan Rajmohan" ]
https://openreview.net/forum?id=zP6DJaBBcR
zP6DJaBBcR
zP6DJaBBcR
[ "~Taneesh_Gupta1", "~Rahul_Madhavan1", "~Xuchao_Zhang1", "~Chetan_Bansal1", "~Saravan_Rajmohan2" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/dc1c639c05cb38e22dd2b44774293041d8886ec9.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Model Alignment", "RLHF", "Preference Optimization" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ gupta2025refa, title={{REFA}: Reference Free Alignment with Fine-Grained Length Control}, author={Taneesh Gupta and Rahul Madhavan and Xuchao Zhang and Chetan Bansal and Saravan Rajmohan}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=zP6DJaBBcR} }
gupta|refa_reference_free_alignment_with_finegrained_length_control
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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution
We propose a fairness benchmark that evaluates intersectional biases in LLMs based on disparities in model confidence while performing coreference resolution on different intersectional identities
Large language models (LLMs) have achieved impressive performance, leading to their widespread adoption as decision-support tools in resource-constrained contexts like hiring and admissions. There is, however, scientific consensus that AI systems can reflect and exacerbate societal biases, raising concerns about identi...
[ "Falaah Arif Khan", "Nivedha Sivakumar", "Yinong Oliver Wang", "Katherine Metcalf", "Cezanne Camacho", "Barry-John Theobald", "Luca Zappella", "Nicholas Apostoloff" ]
https://openreview.net/forum?id=zOw2it5Ni6
zOw2it5Ni6
zOw2it5Ni6
[ "~Falaah_Arif_Khan1", "~Nivedha_Sivakumar1", "~Yinong_Oliver_Wang1", "~Katherine_Metcalf1", "~Cezanne_Camacho1", "~Barry-John_Theobald1", "~Luca_Zappella1", "~Nicholas_Apostoloff1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/036165617f0c285d7a8f21490ec7432f50b68556.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "fairness", "uncertainty", "intersectionality" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ khan2025investigating, title={Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution}, author={Falaah Arif Khan and Nivedha Sivakumar and Yinong Oliver Wang and Katherine Metcalf and Cezanne Camacho and Barry-John Theobald and Luca Zappella and ...
khan|investigating_intersectional_bias_in_large_language_models_using_confidence_disparities_in_coreference_resolution
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MeMAD: Structured Memory of Debates for Enhanced Multi-Agent Reasoning
We propose Memory-Augmented Multi-Agent Debate (MeMAD), which systematically organizes and reuses past debate transcripts to improve performance on complex reasoning tasks without requiring parameter updates.
Large Language Models (LLMs) demonstrate remarkable in-context learning capabilities but often struggle with complex, multi-step reasoning. Multi-Agent Debate (MAD) frameworks partially address these limitations by enabling iterative agent interactions. However, they neglect valuable historical insights by treating eac...
[ "Shuai Ling", "Lizi Liao", "Dongmei Jiang", "Weili Guan" ]
https://openreview.net/forum?id=zLbmsdyTiN
zLbmsdyTiN
zLbmsdyTiN
[ "~Shuai_Ling1", "~Lizi_Liao1", "~Dongmei_Jiang2", "~Weili_Guan4" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/a0b1930d621647e4dd73a698d9879287d9ebcb8d.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Multi-Agent Debate", "Memory Augmentation" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ ling2025memad, title={Me{MAD}: Structured Memory of Debates for Enhanced Multi-Agent Reasoning}, author={Shuai Ling and Lizi Liao and Dongmei Jiang and Weili Guan}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=zLbmsdyTiN} }
ling|memad_structured_memory_of_debates_for_enhanced_multiagent_reasoning
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Values in the Wild: Discovering and Mapping Values in Real-World Language Model Interactions
Our privacy-preserving analysis of values in real-world language model interactions reveals a novel taxonomy of AI values that differs from human frameworks, is highly context-dependent, and becomes most explicit/legible during moments of resistance.
AI assistants interact with millions of real users everyday, imparting normative judgments that can have significant personal and societal impact—but little is known about what values guide these interactions in practice. To address this, we develop a method to empirically analyze values expressed in hundreds of thousa...
[ "Saffron Huang", "Esin DURMUS", "Kunal Handa", "Miles McCain", "Alex Tamkin", "Michael Stern", "Jerry Hong", "Deep Ganguli" ]
https://openreview.net/forum?id=zJHZJClG1Z
zJHZJClG1Z
zJHZJClG1Z
[ "~Saffron_Huang1", "~Esin_DURMUS2", "~Kunal_Handa1", "~Miles_McCain1", "~Alex_Tamkin1", "~Michael_Stern1", "~Jerry_Hong1", "~Deep_Ganguli2" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/d9cb13b573fe0a00779ee4fba3d32dee93dce2d3.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "language models", "values", "AI ethics", "AI values", "empirical analysis", "human-AI interaction", "value alignment", "privacy-preserving analysis", "value pluralism", "AI and society" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ huang2025values, title={Values in the Wild: Discovering and Mapping Values in Real-World Language Model Interactions}, author={Saffron Huang and Esin DURMUS and Kunal Handa and Miles McCain and Alex Tamkin and Michael Stern and Jerry Hong and Deep Ganguli}, booktitle={Second Conference on Language Model...
huang|values_in_the_wild_discovering_and_mapping_values_in_realworld_language_model_interactions
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Deep Binding of Language Model Virtual Personas: a Study on Approximating Political Partisan Misperceptions
We propose a method to build virtual personas for deeper user binding and demonstrate its superiority in approximating metaperception in political science.
Large language models (LLMs) are increasingly capable of simulating human behavior, offering cost-effective ways to estimate user responses during the early phases of survey design. While previous studies have examined whether models can reflect individual opinions or attitudes, we argue that a higher-order binding of ...
[ "Minwoo Kang", "Suhong Moon", "Seung Hyeong Lee", "Ayush Raj", "Joseph Suh", "David Chan" ]
https://openreview.net/forum?id=zHdSCtNmM4
zHdSCtNmM4
zHdSCtNmM4
[ "~Minwoo_Kang1", "~Suhong_Moon1", "~Seung_Hyeong_Lee1", "~Ayush_Raj3", "~Joseph_Suh1", "~David_Chan3" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/08c4c62957f19503b9cb14a781f9366ed5d2ff58.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "user approximation", "metaperception", "social psycholog", "democratic backsliding", "outgroup hostility" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ kang2025deep, title={Deep Binding of Language Model Virtual Personas: a Study on Approximating Political Partisan Misperceptions}, author={Minwoo Kang and Suhong Moon and Seung Hyeong Lee and Ayush Raj and Joseph Suh and David Chan}, booktitle={Second Conference on Language Modeling}, year={2025}, url={...
kang|deep_binding_of_language_model_virtual_personas_a_study_on_approximating_political_partisan_misperceptions
/attachment/887605a60f6440213636e72d74e01e35df30335b.zip
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QUDsim: Quantifying Discourse Similarities in LLM-Generated Text
We introduce an abstraction based on linguistics theories in Questions Under Discussion (QUD) and question semantics to quantify repetitive discourse structures found in texts generated by large language models.
As large language models become increasingly capable at various tasks including writing, the need to generate unique and creative content arises. Although LLMs have the ability to generate text covering diverse topics, there is an overall sense of repetitiveness across texts that we aim to formalize. Such familiarity b...
[ "Ramya Namuduri", "Yating Wu", "Anshun Asher Zheng", "Manya Wadhwa", "Greg Durrett", "Junyi Jessy Li" ]
https://openreview.net/forum?id=zFz1BJu211
zFz1BJu211
zFz1BJu211
[ "~Ramya_Namuduri1", "~Yating_Wu1", "~Anshun_Asher_Zheng1", "~Manya_Wadhwa1", "~Greg_Durrett1", "~Junyi_Jessy_Li2" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/5974197cbc65ce889f5cb6d2e8a8bf2a2394d65c.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "discourse diversity", "discourse structure", "large language models", "Questions Under Discussion" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ namuduri2025qudsim, title={{QUD}sim: Quantifying Discourse Similarities in {LLM}-Generated Text}, author={Ramya Namuduri and Yating Wu and Anshun Asher Zheng and Manya Wadhwa and Greg Durrett and Junyi Jessy Li}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.ne...
namuduri|qudsim_quantifying_discourse_similarities_in_llmgenerated_text
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Probing then Editing Response Personality of Large Language Models
This paper introduces a layer-wise probing framework revealing how LLMs encode personality traits within parameters and further proposes a progressive perturbation method that edits personality during inference using the probing classifier.
Large Language Models (LLMs) have demonstrated promising capabilities to generate responses that simulate consistent personality traits. Despite the major attempts to analyze personality expression through output-based evaluations, little is known about how such traits are internally encoded within LLM parameters. In ...
[ "Tianjie Ju", "Zhenyu Shao", "Bowen Wang", "Yujia Chen", "Zhuosheng Zhang", "Hao Fei", "Mong-Li Lee", "Wynne Hsu", "Sufeng Duan", "Gongshen Liu" ]
https://openreview.net/forum?id=z9SbcYYP0M
z9SbcYYP0M
z9SbcYYP0M
[ "~Tianjie_Ju1", "~Zhenyu_Shao2", "~Bowen_Wang10", "~Yujia_Chen5", "~Zhuosheng_Zhang1", "~Hao_Fei1", "~Mong-Li_Lee1", "~Wynne_Hsu1", "~Sufeng_Duan1", "~Gongshen_Liu2" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/498cc4dc5e0ee0f10ac6252c267617bc18a5cc09.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "large language model", "personality", "interpretability", "knowledge editing" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ ju2025probing, title={Probing then Editing Response Personality of Large Language Models}, author={Tianjie Ju and Zhenyu Shao and Bowen Wang and Yujia Chen and Zhuosheng Zhang and Hao Fei and Mong-Li Lee and Wynne Hsu and Sufeng Duan and Gongshen Liu}, booktitle={Second Conference on Language Modeling},...
ju|probing_then_editing_response_personality_of_large_language_models
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CodeXEmbed: A Generalist Embedding Model Family for Multilingual and Multi-task Code Retrieval
We introduce CodeXEmbed, a large-scale code embedding model achieving SOTA on CoIR and strong BeIR performance, enhancing code retrieval and RAG.
Despite the success of text retrieval in many NLP tasks, code retrieval remains a largely underexplored area. Most text retrieval systems are tailored for natural language queries, often neglecting the specific challenges of retrieving code. This gap leaves existing models unable to effectively capture the diversity of...
[ "Ye Liu", "Rui Meng", "Shafiq Joty", "silvio savarese", "Caiming Xiong", "Yingbo Zhou", "Semih Yavuz" ]
https://openreview.net/forum?id=z3lG70Azbg
z3lG70Azbg
z3lG70Azbg
[ "~Ye_Liu4", "~Rui_Meng1", "~Shafiq_Joty1", "~silvio_savarese2", "~Caiming_Xiong1", "~Yingbo_Zhou1", "~Semih_Yavuz1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/556d386c8c3457c2f182c786da442e3b3cbd673a.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Code and Text Retrieval; Code Embedding Model; Text Embedding Model; Retrieval-Augmented Code Generation" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ liu2025codexembed, title={Code{XE}mbed: A Generalist Embedding Model Family for Multilingual and Multi-task Code Retrieval}, author={Ye Liu and Rui Meng and Shafiq Joty and silvio savarese and Caiming Xiong and Yingbo Zhou and Semih Yavuz}, booktitle={Second Conference on Language Modeling}, year={2025}...
liu|codexembed_a_generalist_embedding_model_family_for_multilingual_and_multitask_code_retrieval
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Retrieval-Augmented Generation with Conflicting Evidence
We propose a benchmark and multi-agent framework for RAG systems to handle ambiguity, conflicting evidence, and misinformation in real-world retrieval scenarios.
Large language model (LLM) agents are increasingly employing retrieval-augmented generation (RAG) to improve the factuality of their responses. However, in practice, these systems often need to handle ambiguous user queries and potentially conflicting information from multiple sources while also suppressing inaccurate ...
[ "Han Wang", "Archiki Prasad", "Elias Stengel-Eskin", "Mohit Bansal" ]
https://openreview.net/forum?id=z1MHB2m3V9
z1MHB2m3V9
z1MHB2m3V9
[ "~Han_Wang9", "~Archiki_Prasad1", "~Elias_Stengel-Eskin1", "~Mohit_Bansal2" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/1452223fd8e091c35c8990c14f2d5e4979e749bc.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Retrieval-augmented Generation", "Knowledge Conflict", "Multi-agent" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ wang2025retrievalaugmented, title={Retrieval-Augmented Generation with Conflicting Evidence}, author={Han Wang and Archiki Prasad and Elias Stengel-Eskin and Mohit Bansal}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=z1MHB2m3V9} }
wang|retrievalaugmented_generation_with_conflicting_evidence
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Déjà Vu: Multilingual LLM Evaluation through the Lens of Machine Translation Evaluation
What can multilingual LLM evaluation learn from MT evaluation?
Generation capabilities and language coverage of multilingual large language models (mLLMs) are advancing rapidly. However, evaluation practices for generative abilities of mLLMs are still lacking comprehensiveness, scientific rigor, and consistent adoption across research labs, which undermines their potential to mean...
[ "Julia Kreutzer", "Eleftheria Briakou", "Sweta Agrawal", "Marzieh Fadaee", "Tom Kocmi" ]
https://openreview.net/forum?id=yxzVanFoij
yxzVanFoij
yxzVanFoij
[ "~Julia_Kreutzer1", "~Eleftheria_Briakou1", "~Sweta_Agrawal1", "~Marzieh_Fadaee2", "~Tom_Kocmi1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/d390b099c4e0bc139be3a1a975837803ed0bf6db.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "multilingual", "evaluation", "meta-evaluation", "machine translation evaluation" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ kreutzer2025dj, title={D\'ej\`a Vu: Multilingual {LLM} Evaluation through the Lens of Machine Translation Evaluation}, author={Julia Kreutzer and Eleftheria Briakou and Sweta Agrawal and Marzieh Fadaee and Tom Kocmi}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openrevi...
kreutzer|déjà_vu_multilingual_llm_evaluation_through_the_lens_of_machine_translation_evaluation
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CONCAP: Seeing Beyond English with Concepts Retrieval-Augmented Captioning
Image captioning with concept and captions retrieval augmented generation.
Multilingual vision-language models have made significant strides in image captioning, yet they still lag behind their English counterparts due to limited multilingual training data and costly large-scale model parameterization. Retrieval-augmented generation (RAG) offers a promising alternative by conditioning caption...
[ "George Ibrahim", "Rita Ramos", "Yova Kementchedjhieva" ]
https://openreview.net/forum?id=yfnaK1pZxu
yfnaK1pZxu
yfnaK1pZxu
[ "~George_Ibrahim1", "~Rita_Ramos1", "~Yova_Kementchedjhieva1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/b261ae04f4cd4225e18480084dc5543014d43819.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Image Captioning", "Concepts", "Retrieval", "RAG", "Multilingual" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ ibrahim2025concap, title={{CONCAP}: Seeing Beyond English with Concepts Retrieval-Augmented Captioning}, author={George Ibrahim and Rita Ramos and Yova Kementchedjhieva}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=yfnaK1pZxu} }
ibrahim|concap_seeing_beyond_english_with_concepts_retrievalaugmented_captioning
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Prompt-Reverse Inconsistency: LLM Self-Inconsistency Beyond Generative Randomness and Prompt Paraphrasing
This paper introduces Prompt-Reverse Inconsistency (PRIN), where Large Language Models give conflicting answers when identifying correct versus incorrect responses, raising concerns about their logical reliability.
While the inconsistency of LLMs is not a novel topic, prior research has predominantly addressed two types of generative inconsistencies: i) Randomness Inconsistency: running the same LLM multiple trials, yielding varying responses; ii) Paraphrase Inconsistency: paraphrased prompts result in different responses from th...
[ "Jihyun Janice Ahn", "Wenpeng Yin" ]
https://openreview.net/forum?id=yfRkNRFLzl
yfRkNRFLzl
yfRkNRFLzl
[ "~Jihyun_Janice_Ahn1", "~Wenpeng_Yin1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/44b6a89c82f5042a567ea17d3070285af52ead04.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Large Language Model", "Natural Language Process", "Inconsistency of LLMs", "Prompt-Reverse Inconsistency", "Randomness Inconsistency", "Paraphrase Inconsistency" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ ahn2025promptreverse, title={Prompt-Reverse Inconsistency: {LLM} Self-Inconsistency Beyond Generative Randomness and Prompt Paraphrasing}, author={Jihyun Janice Ahn and Wenpeng Yin}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=yfRkNRFLzl} }
ahn|promptreverse_inconsistency_llm_selfinconsistency_beyond_generative_randomness_and_prompt_paraphrasing
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Learning to Generate Unit Tests for Automated Debugging
LLM training pipeline for generating unit tests for code debugging and assessing code correctness
Unit tests (UTs) play an instrumental role in assessing code correctness as well as providing feedback to large language models (LLMs), motivating automated test generation. However, we uncover a trade-off between generating unit test inputs that reveal errors when given a faulty code and correctly predicting the unit ...
[ "Archiki Prasad", "Elias Stengel-Eskin", "Justin Chen", "Zaid Khan", "Mohit Bansal" ]
https://openreview.net/forum?id=yeVBHPLXxi
yeVBHPLXxi
yeVBHPLXxi
[ "~Archiki_Prasad1", "~Elias_Stengel-Eskin1", "~Justin_Chen1", "~Zaid_Khan1", "~Mohit_Bansal2" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/d0e3dc4e72f75d7f7f18fe8c0ab78512b18f88a0.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Unit Tests Generation", "LLMs for code generation", "LLMs for code debugging" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ prasad2025learning, title={Learning to Generate Unit Tests for Automated Debugging}, author={Archiki Prasad and Elias Stengel-Eskin and Justin Chen and Zaid Khan and Mohit Bansal}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=yeVBHPLXxi} }
prasad|learning_to_generate_unit_tests_for_automated_debugging
/attachment/f3e3abd874e6c1ad0a461bbcbb6c9cf7fe922262.zip
null
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VideoSAVi: Self-Aligned Video Language Models without Human Supervision
VideoSAVi introduces a self-aligning approach that enables video-language models to generate high-quality preference pairs from their own outputs, achieving state-of-the-art performance without external supervision.
Recent advances in video-large language models (Video-LLMs) have led to significant progress in video understanding. Current preference optimization methods often rely on proprietary APIs or ground-truth captions to generate preference data (i.e., pairs of model outputs ranked based on their quality or alignment with h...
[ "Yogesh Kulkarni", "Pooyan Fazli" ]
https://openreview.net/forum?id=ybcZEWaM7U
ybcZEWaM7U
ybcZEWaM7U
[ "~Yogesh_Kulkarni1", "~Pooyan_Fazli1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/5846d0a6f494f556df2f25ba346da586faf3d28b.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Video understanding", "Self-alignment", "Video-language models", "Direct preference optimization", "Self-critiquing" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ kulkarni2025videosavi, title={Video{SAV}i: Self-Aligned Video Language Models without Human Supervision}, author={Yogesh Kulkarni and Pooyan Fazli}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=ybcZEWaM7U} }
kulkarni|videosavi_selfaligned_video_language_models_without_human_supervision
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Streaming DiLoCo with overlapping communication
Distributed training where only a subset of the outer gradients is communicated
Training of large language models (LLMs) is typically distributed across a large number of accelerators to reduce training time. Since internal states and parameter gradients need to be exchanged at each and every single gradient step, all devices need to be co-located using low-latency high-bandwidth communication lin...
[ "Arthur Douillard", "Yani Donchev", "J Keith Rush", "Satyen Kale", "Zachary Charles", "Gabriel Teston", "Zachary Garrett", "Jiajun Shen", "Ross McIlroy", "David Lacey", "Alexandre Rame", "Arthur Szlam", "MarcAurelio Ranzato", "Paul R Barham" ]
https://openreview.net/forum?id=yYk3zK0X6Q
yYk3zK0X6Q
yYk3zK0X6Q
[ "~Arthur_Douillard1", "~Yani_Donchev1", "~J_Keith_Rush1", "~Satyen_Kale2", "~Zachary_Charles1", "~Gabriel_Teston1", "~Zachary_Garrett1", "~Jiajun_Shen1", "~Ross_McIlroy1", "~David_Lacey1", "~Alexandre_Rame1", "~Arthur_Szlam3", "~MarcAurelio_Ranzato1", "~Paul_R_Barham2" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/cfdba2f22f8184c00ce2bee496c5685b22b6922e.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "distributed training", "large-scale", "llm" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ douillard2025streaming, title={Streaming DiLoCo with overlapping communication}, author={Arthur Douillard and Yani Donchev and J Keith Rush and Satyen Kale and Zachary Charles and Gabriel Teston and Zachary Garrett and Jiajun Shen and Ross McIlroy and David Lacey and Alexandre Rame and Arthur Szlam and ...
douillard|streaming_diloco_with_overlapping_communication
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Multilingual and Multi-Accent Jailbreaking of Audio LLMs
We propose Multi-AudioJail --- a novel audio jailbreak attack that exploits multilingual and multi-accent audio inputs enhanced with audio adversarial perturbations.
Large Audio Language Models (LALMs) have significantly advanced audio understanding but introduce critical security risks, particularly through audio jailbreaks. While prior work has focused on English-centric attacks, we expose a far more severe vulnerability: adversarial multilingual and multi-accent audio jailbreaks...
[ "Jaechul Roh", "Virat Shejwalkar", "Amir Houmansadr" ]
https://openreview.net/forum?id=yGa8CYT8kS
yGa8CYT8kS
yGa8CYT8kS
[ "~Jaechul_Roh1", "~Virat_Shejwalkar1", "~Amir_Houmansadr1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/6147a6961d7ae656a86a7d1238824560951dada6.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Audio", "LLM", "Jailbreak", "Multilingual", "Security" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
true
the audio jailbreaking might be offensive to some audience.
@inproceedings{ roh2025multilingual, title={Multilingual and Multi-Accent Jailbreaking of Audio {LLM}s}, author={Jaechul Roh and Virat Shejwalkar and Amir Houmansadr}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=yGa8CYT8kS} }
roh|multilingual_and_multiaccent_jailbreaking_of_audio_llms
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Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models
We introduce a novel inference-time algorithm, ThoughtTracing, which uses LLMs to probabilistically trace and weight hypotheses about agents’ evolving mental states without relying on questions and ground-truth answers in benchmarks.
Existing LLM reasoning methods have shown impressive capabilities across various tasks, such as solving math and coding problems. However, applying these methods to scenarios without ground-truth answers or rule-based verification methods - such as tracking the mental states of an agent - remains challenging. Inspired ...
[ "Hyunwoo Kim", "Melanie Sclar", "Tan Zhi-Xuan", "Lance Ying", "Sydney Levine", "Yang Liu", "Joshua B. Tenenbaum", "Yejin Choi" ]
https://openreview.net/forum?id=yGQqTuSJPK
yGQqTuSJPK
yGQqTuSJPK
[ "~Hyunwoo_Kim3", "~Melanie_Sclar1", "~Tan_Zhi-Xuan1", "~Lance_Ying1", "~Sydney_Levine1", "~Yang_Liu60", "~Joshua_B._Tenenbaum1", "~Yejin_Choi1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/5d8ab1d8f4c22c924ea0d6ee9ae0a18ef00a958b.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "theory of mind", "reasoning", "large language model", "inference-time algorithm" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ kim2025hypothesisdriven, title={Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models}, author={Hyunwoo Kim and Melanie Sclar and Tan Zhi-Xuan and Lance Ying and Sydney Levine and Yang Liu and Joshua B. Tenenbaum and Yejin Choi}, booktitle={Second Conference on Language Modeling}, year={2...
kim|hypothesisdriven_theoryofmind_reasoning_for_large_language_models
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IterKey: Iterative Keyword Generation with LLMs for Enhanced Retrieval Augmented Generation
We introduce IterKey, an LLM-based iterative keyword generation method that optimize the Retrieval-Augmented Generation process, improving accuracy by refining keywords and self-evaluating responses.
Retrieval Augmented Generation (RAG) has emerged as a way to complement the in-context knowledge of Large Language Models (LLMs) by integrating external documents. However, real-world applications demand not only accuracy but also interpretability. Dense retrieval methods provide high accuracy but lack interpretabili...
[ "Kazuki Hayashi", "Hidetaka Kamigaito", "Shinya Kouda", "Taro Watanabe" ]
https://openreview.net/forum?id=y56BuSo8Uj
y56BuSo8Uj
y56BuSo8Uj
[ "~Kazuki_Hayashi1", "~Hidetaka_Kamigaito2", "~Shinya_Kouda1", "~Taro_Watanabe1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/89b81fc6b363dd0e2529ebd9dbc0474cdee121a7.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "retrieval-augmented generation", "RAG", "sparse retrieval", "LLM", "Iterative" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ hayashi2025iterkey, title={IterKey: Iterative Keyword Generation with {LLM}s for Enhanced Retrieval Augmented Generation}, author={Kazuki Hayashi and Hidetaka Kamigaito and Shinya Kouda and Taro Watanabe}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum...
hayashi|iterkey_iterative_keyword_generation_with_llms_for_enhanced_retrieval_augmented_generation
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Can LLM "Self-report"?: Evaluating the Validity of Self-report Scales in Measuring Personality Design in LLM-based Chatbots
Evaluating the Validity of Self-report Scales in Measuring Personality Design in LLM-based Chatbots
A chatbot’s personality design is key to interaction quality. As chatbots evolved from rule-based systems to those powered by large language models (LLMs), evaluating the effectiveness of their personality design has become increasingly complex, particularly due to the open-ended nature of interactions. A recent and wi...
[ "Huiqi Zou", "Pengda Wang", "Zihan Yan", "Tianjun Sun", "Ziang Xiao" ]
https://openreview.net/forum?id=xqIwK9mNkj
xqIwK9mNkj
xqIwK9mNkj
[ "~Huiqi_Zou1", "~Pengda_Wang1", "~Zihan_Yan1", "~Tianjun_Sun1", "~Ziang_Xiao1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/388bb244be1dd675a49c4295cc3cd296fe6906a1.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "human factors in NLP; evaluation methodologies" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ zou2025can, title={Can {LLM} ''Self-report''?: Evaluating the Validity of Self-report Scales in Measuring Personality Design in {LLM}-based Chatbots}, author={Huiqi Zou and Pengda Wang and Zihan Yan and Tianjun Sun and Ziang Xiao}, booktitle={Second Conference on Language Modeling}, year={2025}, url={ht...
zou|can_llm_selfreport_evaluating_the_validity_of_selfreport_scales_in_measuring_personality_design_in_llmbased_chatbots
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Always Tell Me The Odds: Fine-grained Conditional Probability Estimation
We present a state-of-the-art model for fine-grained probability estimation of textual outcomes conditioned on context.
We present a state-of-the-art model for fine-grained probability estimation of propositions conditioned on context. Recent advances in large language models (LLMs) have significantly enhanced their reasoning capabilities, particularly on well-defined tasks with complete information. However, LLMs continue to struggle w...
[ "Liaoyaqi Wang", "Zhengping Jiang", "Anqi Liu", "Benjamin Van Durme" ]
https://openreview.net/forum?id=xhDcG8qtw9
xhDcG8qtw9
xhDcG8qtw9
[ "~Liaoyaqi_Wang1", "~Zhengping_Jiang1", "~Anqi_Liu2", "~Benjamin_Van_Durme2" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/c53d10b668e237deedcef8b115cc9a506df677e2.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Large Language Model", "Probabilistic Reasoning", "Semantics", "Calibration" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ wang2025always, title={Always Tell Me The Odds: Fine-grained Conditional Probability Estimation}, author={Liaoyaqi Wang and Zhengping Jiang and Anqi Liu and Benjamin Van Durme}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=xhDcG8qtw9} }
wang|always_tell_me_the_odds_finegrained_conditional_probability_estimation
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CALLME: Call Graph Augmentation with Large Language Models for Javascript
Handling edge cases in call graph construction for Javascript that cannot be handled with static analysis using large language models.
Building precise call graphs for Javascript programs is a fundamental build- ing block for many important software engineering and security applications such as bug detection, program repair, and refactoring. However, resolving dynamic calls using static analysis is challenging because it requires enumerating all possi...
[ "Michael Wang", "Kexin Pei", "Armando Solar-Lezama" ]
https://openreview.net/forum?id=xZi2rMUcAO
xZi2rMUcAO
xZi2rMUcAO
[ "~Michael_Wang1", "~Kexin_Pei1", "~Armando_Solar-Lezama1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/41f82f781ab3f27465ee5b45ef2e3218c13a0f63.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Javascript", "program analysis" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ wang2025callme, title={{CALLME}: Call Graph Augmentation with Large Language Models for Javascript}, author={Michael Wang and Kexin Pei and Armando Solar-Lezama}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=xZi2rMUcAO} }
wang|callme_call_graph_augmentation_with_large_language_models_for_javascript
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Adaptive Computation Pruning for the Forgetting Transformer
We propose a method that adaptively prunes computations in the Forgetting Transformer based on forget gate values.
The recently proposed Forgetting Transformer (FoX) incorporates a forget gate into softmax attention and has shown consistently better or on-par performance compared to the standard RoPE-based Transformer. Notably, many attention heads in FoX tend to forget quickly, causing their output at each timestep to rely primari...
[ "Zhixuan Lin", "Johan Obando-Ceron", "Xu Owen He", "Aaron Courville" ]
https://openreview.net/forum?id=xNj14CY5S1
xNj14CY5S1
xNj14CY5S1
[ "~Zhixuan_Lin1", "~Johan_Obando-Ceron1", "~Xu_Owen_He1", "~Aaron_Courville3" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/dabdb7eb3af1dbd15e1d91d6bb30f2a59bd77334.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "transformer", "forgetting transformer", "efficient transformer", "sequence modeling", "adaptive computation pruning", "forget gate", "sparse attention", "FlashAttention", "hardware-aware optimization" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ lin2025adaptive, title={Adaptive Computation Pruning for the Forgetting Transformer}, author={Zhixuan Lin and Johan Obando-Ceron and Xu Owen He and Aaron Courville}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=xNj14CY5S1} }
lin|adaptive_computation_pruning_for_the_forgetting_transformer
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Energy-Based Reward Models for Robust Language Model Alignment
We introduce Energy-Based Reward Model (EBRM), a post-hoc method to refine reward models using EBMs.
Reward models (RMs) are essential for aligning Large Language Models (LLMs) with human preference. However, they often struggle with capturing complex human preferences and generalizing to unseen data. To address these challenges, we introduce \emph{Energy-Based Reward Model} (EBRM), a lightweight post-hoc refinement f...
[ "Anamika Lochab", "Ruqi Zhang" ]
https://openreview.net/forum?id=x6evCULIOQ
x6evCULIOQ
x6evCULIOQ
[ "~Anamika_Lochab1", "~Ruqi_Zhang1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/0cbcf905a030463b01e16ef7de935a5d0ef38517.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Reward Models", "Alignment", "Energy Based Models" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ lochab2025energybased, title={Energy-Based Reward Models for Robust Language Model Alignment}, author={Anamika Lochab and Ruqi Zhang}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=x6evCULIOQ} }
lochab|energybased_reward_models_for_robust_language_model_alignment
/attachment/d94995fd2c118eb604ec15b3b03867d739a2ce0c.zip
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Guided Reasoning in LLM-Driven Penetration Testing Using Structured Attack Trees
We propose a reasoning pipeline for penetration testing LLM agents using a structured task tree based on proven cybersecurity kill chains. Our method achieves 74.4% attack subtask completion (vs. 35.2% by the SOTA) and requires 55.9% fewer queries.
Recent advances in large language models (LLMs) have driven interest in automating cybersecurity penetration testing workflows, offering the promise of faster and more consistent vulnerability assessment for enterprise systems. Existing LLM agents for penetration testing primarily rely on self‐guided reasoning, which c...
[ "Katsuaki Nakano", "Reza Fayyazi", "Shanchieh Yang", "Michael Zuzak" ]
https://openreview.net/forum?id=x4sdXZ7Jdu
x4sdXZ7Jdu
x4sdXZ7Jdu
[ "~Katsuaki_Nakano1", "~Reza_Fayyazi1", "~Shanchieh_Yang1", "~Michael_Zuzak1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/972da2fcf7e33064c89c2aed8f19ab736d656ab1.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Penetration Testing", "Large Language Models", "Autonomous Penetration Testing Agents" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ nakano2025guided, title={Guided Reasoning in {LLM}-Driven Penetration Testing Using Structured Attack Trees}, author={Katsuaki Nakano and Reza Fayyazi and Shanchieh Yang and Michael Zuzak}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=x4sdXZ7Jdu} ...
nakano|guided_reasoning_in_llmdriven_penetration_testing_using_structured_attack_trees
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Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving
Introduce Goedel-Prover, an open-source language model that achieves SOTA in automated theorem proving in Lean
We introduce Goedel-Prover, an open-source language model that achieves state-of-the-art performance in automated formal proof generation for mathematical problems. A key challenge in this field is the scarcity of formalized mathematical statements and proofs, which we address through the following approaches. First, ...
[ "Yong Lin", "Shange Tang", "Bohan Lyu", "Jiayun Wu", "Hongzhou Lin", "Kaiyu Yang", "Jia LI", "Mengzhou Xia", "Danqi Chen", "Sanjeev Arora", "Chi Jin" ]
https://openreview.net/forum?id=x2y9i2HDjD
x2y9i2HDjD
x2y9i2HDjD
[ "~Yong_Lin2", "~Shange_Tang1", "~Bohan_Lyu1", "~Jiayun_Wu1", "~Hongzhou_Lin1", "~Kaiyu_Yang1", "~Jia_LI18", "~Mengzhou_Xia1", "~Danqi_Chen1", "~Sanjeev_Arora1", "~Chi_Jin1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/047be6bba4d4ce1c4aae8dfe2c987deef335ef45.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Formal reasoning", "verification", "Lean", "self-improvement" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ lin2025goedelprover, title={Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving}, author={Yong Lin and Shange Tang and Bohan Lyu and Jiayun Wu and Hongzhou Lin and Kaiyu Yang and Jia LI and Mengzhou Xia and Danqi Chen and Sanjeev Arora and Chi Jin}, booktitle={Second Conference on ...
lin|goedelprover_a_frontier_model_for_opensource_automated_theorem_proving
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EnrichIndex: Using LLMs to Enrich Retrieval Indices Offline
EnrichIndex enriches documents offline using LLMs, improving retrieval performance on complex retrieval tasks with significantly lower latency and online cost.
Existing information retrieval systems excel in cases where the language of target documents closely matches that of the user query. However, real-world retrieval systems are often required to *implicitly reason* whether a document is relevant. For example, when retrieving technical texts or tables, their relevance to ...
[ "Peter Baile Chen", "Tomer Wolfson", "Mike Cafarella", "Dan Roth" ]
https://openreview.net/forum?id=wyYL5Jov6e
wyYL5Jov6e
wyYL5Jov6e
[ "~Peter_Baile_Chen1", "~Tomer_Wolfson1", "~Mike_Cafarella1", "~Dan_Roth3" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/7e22d77624c4e6b84c79cf58406e46c07740df64.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "retrieval", "offline enrichment", "implicit reasoning" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ chen2025enrichindex, title={EnrichIndex: Using {LLM}s to Enrich Retrieval Indices Offline}, author={Peter Baile Chen and Tomer Wolfson and Mike Cafarella and Dan Roth}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=wyYL5Jov6e} }
chen|enrichindex_using_llms_to_enrich_retrieval_indices_offline
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Elucidating the Design Space of Decay in Linear Attention
Elucidating the Design Space of Decay in Linear Attention
This paper presents a comprehensive investigation into the decay mechanisms inherent in linear complexity sequence models. We systematically delineate the design space of decay mechanisms across four pivotal dimensions: parameterization strategy, which refers to the computational methodology for decay; parameter sharin...
[ "Zhen Qin", "Xuyang Shen", "Yiran Zhong" ]
https://openreview.net/forum?id=whXh2YxMbt
whXh2YxMbt
whXh2YxMbt
[ "~Zhen_Qin6", "~Xuyang_Shen1", "~Yiran_Zhong1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/815a0f2594e2d32ff855a8a9d677e3855205a1c4.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Linear Attention" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ qin2025elucidating, title={Elucidating the Design Space of Decay in Linear Attention}, author={Zhen Qin and Xuyang Shen and Yiran Zhong}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=whXh2YxMbt} }
qin|elucidating_the_design_space_of_decay_in_linear_attention
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PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages
We introduce PolyGuard, a new state-of-the-art multilingual safety model for safeguarding LLM generations along with PolyGuardMix for safety detection training and PolyGuardPrompts for safety guardrail evaluation.
Truly multilingual safety moderation efforts for Large Language Models (LLMs) have been hindered by a narrow focus on a small set of languages (e.g., English, Chinese) as well as a limited scope of safety definition, resulting in significant gaps in moderation capabilities. To bridge these gaps, we release POLYGUARD, a...
[ "Priyanshu Kumar", "Devansh Jain", "Akhila Yerukola", "Liwei Jiang", "Himanshu Beniwal", "Thomas Hartvigsen", "Maarten Sap" ]
https://openreview.net/forum?id=wbAWKXNeQ4
wbAWKXNeQ4
wbAWKXNeQ4
[ "~Priyanshu_Kumar1", "~Devansh_Jain1", "~Akhila_Yerukola1", "~Liwei_Jiang2", "~Himanshu_Beniwal1", "~Thomas_Hartvigsen1", "~Maarten_Sap1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/083b5e10f049e428ff125b8665aea78cf0284e75.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "ai safety", "hate-speech detection" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ kumar2025polyguard, title={PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages}, author={Priyanshu Kumar and Devansh Jain and Akhila Yerukola and Liwei Jiang and Himanshu Beniwal and Thomas Hartvigsen and Maarten Sap}, booktitle={Second Conference on Language Modeling}, year={2025}, url={h...
kumar|polyguard_a_multilingual_safety_moderation_tool_for_17_languages
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More is Less: The Pitfalls of Multi-Model Synthetic Preference Data in DPO Safety Alignment
LLMs learns about safety better from their own outputs than from others.
Aligning large language models (LLMs) with human values is an increasingly critical step in post-training. Direct Preference Optimization (DPO) has emerged as a simple, yet effective alternative to reinforcement learning from human feedback (RLHF). Synthetic preference data with its low cost and high quality enable eff...
[ "Yifan Wang", "Runjin Chen", "Bolian Li", "David Cho", "Yihe Deng", "Ruqi Zhang", "Tianlong Chen", "Zhangyang Wang", "Ananth Grama", "Junyuan Hong" ]
https://openreview.net/forum?id=wXOUYzNv5k
wXOUYzNv5k
wXOUYzNv5k
[ "~Yifan_Wang14", "~Runjin_Chen1", "~Bolian_Li1", "~David_Cho2", "~Yihe_Deng1", "~Ruqi_Zhang1", "~Tianlong_Chen1", "~Zhangyang_Wang1", "~Ananth_Grama1", "~Junyuan_Hong1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/094c4e24c1d72adc8f0ceeded062c9fdf6235d7b.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Alignment", "Synthetic Data", "Safety", "Large Language Models" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ wang2025more, title={More is Less: The Pitfalls of Multi-Model Synthetic Preference Data in {DPO} Safety Alignment}, author={Yifan Wang and Runjin Chen and Bolian Li and David Cho and Yihe Deng and Ruqi Zhang and Tianlong Chen and Zhangyang Wang and Ananth Grama and Junyuan Hong}, booktitle={Second Conf...
wang|more_is_less_the_pitfalls_of_multimodel_synthetic_preference_data_in_dpo_safety_alignment
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Sherkala-Chat: Building a State-of-the-Art LLM for Kazakh in a Moderately Resourced Setting
Sherkala-Chat (8B) is a state-of-the-art, instruction-tuned open LLM for Kazakh, excelling in Kazakh language tasks while remaining competitive in English.
Llama-3.1-Sherkala-8B-Chat, or Sherkala-Chat (8B) for short, is a state-of-the-art instruction-tuned open generative large language model (LLM) designed for Kazakh. Sherkala-Chat (8B) aims to enhance the inclusivity of LLM advancements for Kazakh speakers. Adapted from the LLaMA-3.1-8B model, Sherkala-Chat (8B) is trai...
[ "Fajri Koto", "Rituraj Joshi", "Nurdaulet Mukhituly", "Yuxia Wang", "Zhuohan Xie", "Rahul Pal", "Daniil Orel", "Parvez Mullah", "Diana Turmakhan", "Maiya Goloburda", "Mohammed Kamran", "Samujjwal Ghosh", "Bokang Jia", "Jonibek Mansurov", "Mukhammed Togmanov", "Debopriyo Banerjee", "N...
https://openreview.net/forum?id=wRcTCcb0H5
wRcTCcb0H5
wRcTCcb0H5
[ "~Fajri_Koto1", "~Rituraj_Joshi1", "~Nurdaulet_Mukhituly1", "~Yuxia_Wang1", "~Zhuohan_Xie1", "~Rahul_Pal1", "~Daniil_Orel1", "~Parvez_Mullah1", "~Diana_Turmakhan1", "~Maiya_Goloburda1", "~Mohammed_Kamran1", "~Samujjwal_Ghosh1", "~Bokang_Jia1", "~Jonibek_Mansurov1", "~Mukhammed_Togmanov1"...
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/81303233cce33f5509d80aa0ef16ce80cdba0fb8.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "LLM", "LLaMA-3.1", "Kazakh", "low-resource language modeling", "fine-tuning", "safety alignment", "model evaluation", "generative AI" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ koto2025sherkalachat, title={Sherkala-Chat: Building a State-of-the-Art {LLM} for Kazakh in a Moderately Resourced Setting}, author={Fajri Koto and Rituraj Joshi and Nurdaulet Mukhituly and Yuxia Wang and Zhuohan Xie and Rahul Pal and Daniil Orel and Parvez Mullah and Diana Turmakhan and Maiya Goloburda...
koto|sherkalachat_building_a_stateoftheart_llm_for_kazakh_in_a_moderately_resourced_setting
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Quantifying Fairness in LLMs Beyond Tokens: A Semantic and Statistical Perspective
We introduce a framework for assessing and analyzing bias in long text outputs at group level.
Large Language Models (LLMs) often generate responses with inherent biases, undermining their reliability in real-world applications. Existing evaluation meth- ods often overlook biases in long-form responses and the intrinsic variability of LLM outputs. To address these challenges, we propose FiSCo (Fine-grained Se- m...
[ "Weijie Xu", "Yiwen Wang", "Chi Xue", "Xiangkun Hu", "Xi Fang", "Guimin Dong", "Chandan K. Reddy" ]
https://openreview.net/forum?id=wKVtjs0w4a
wKVtjs0w4a
wKVtjs0w4a
[ "~Weijie_Xu1", "~Yiwen_Wang4", "~Chi_Xue1", "~Xiangkun_Hu1", "~Xi_Fang3", "~Guimin_Dong1", "~Chandan_K._Reddy1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/371c9407949422ef1df4a9f9b27d0ad0aa911a2b.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Fairness", "Bias", "Evaluation" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ xu2025quantifying, title={Quantifying Fairness in {LLM}s Beyond Tokens: A Semantic and Statistical Perspective}, author={Weijie Xu and Yiwen Wang and Chi Xue and Xiangkun Hu and Xi Fang and Guimin Dong and Chandan K. Reddy}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://o...
xu|quantifying_fairness_in_llms_beyond_tokens_a_semantic_and_statistical_perspective
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How Post-Training Reshapes LLMs: A Mechanistic View on Knowledge, Truthfulness, Refusal, and Confidence
We compare base(pretrained) LLM and instruct(post-trained) LLM mechanistically in four perspectives and provide insight to what is preserved and altered.
Post-training is essential for the success of large language models (LLMs), transforming pre-trained base models into more useful and aligned post-trained models. While plenty of works have studied post-training algorithms and evaluated post-training models by their outputs, it remains understudied how post-training re...
[ "Hongzhe Du", "Weikai Li", "Min Cai", "Karim Saraipour", "Zimin Zhang", "Himabindu Lakkaraju", "Yizhou Sun", "Shichang Zhang" ]
https://openreview.net/forum?id=w5DSwn9wTC
w5DSwn9wTC
w5DSwn9wTC
[ "~Hongzhe_Du1", "~Weikai_Li2", "~Min_Cai2", "~Karim_Saraipour1", "~Zimin_Zhang1", "~Himabindu_Lakkaraju1", "~Yizhou_Sun1", "~Shichang_Zhang2" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/ed7d9a75939697cf5c03dbd93501fd32e855ede6.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Mechanistic Interpretability", "Instruction-tuning", "Post-training", "Alignment" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ du2025how, title={How Post-Training Reshapes {LLM}s: A Mechanistic View on Knowledge, Truthfulness, Refusal, and Confidence}, author={Hongzhe Du and Weikai Li and Min Cai and Karim Saraipour and Zimin Zhang and Himabindu Lakkaraju and Yizhou Sun and Shichang Zhang}, booktitle={Second Conference on Langu...
du|how_posttraining_reshapes_llms_a_mechanistic_view_on_knowledge_truthfulness_refusal_and_confidence
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The Zero Body Problem: Probing LLM Use of Sensory Language
Popular large language models fail to replicate human use of sensory language, an important feature of storytelling.
Sensory language expresses embodied experiences ranging from taste and sound to excitement and stomachache. It is of interest to scholars from a wide range of domains including robotics, narratology, linguistics, and cognitive science. In this work, we explore whether language models, which are not embodied, can approx...
[ "Rebecca M. M. Hicke", "Sil Hamilton", "David Mimno" ]
https://openreview.net/forum?id=vv1ZyQF8LD
vv1ZyQF8LD
vv1ZyQF8LD
[ "~Rebecca_M._M._Hicke1", "~Sil_Hamilton1", "~David_Mimno1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/3c070894fc335f646c5bbdc34c6ec387206d6861.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "model evaluation", "model interpretability", "sensory language", "model creativity" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ hicke2025the, title={The Zero Body Problem: Probing {LLM} Use of Sensory Language}, author={Rebecca M. M. Hicke and Sil Hamilton and David Mimno}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=vv1ZyQF8LD} }
hicke|the_zero_body_problem_probing_llm_use_of_sensory_language
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Base Models Beat Aligned Models at Randomness and Creativity
Alignment seems to hurt performance on a set of tasks that require randomness or creativity
Alignment has quickly become a default ingredient in LLM development, with techniques such as reinforcement learning from human feedback making models act safely, follow instructions, and perform ever-better on complex tasks. While these techniques are certainly useful, we propose that they should not be universally ap...
[ "Peter West", "Christopher Potts" ]
https://openreview.net/forum?id=vqN8uom4A1
vqN8uom4A1
vqN8uom4A1
[ "~Peter_West1", "~Christopher_Potts1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/fac784fe4c8e095a2a44374be0e7e9378dc7c012.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "alignment", "pretrained", "limitations", "limits", "capabilities", "randomness", "creativity" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ west2025base, title={Base Models Beat Aligned Models at Randomness and Creativity}, author={Peter West and Christopher Potts}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=vqN8uom4A1} }
west|base_models_beat_aligned_models_at_randomness_and_creativity
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Improving Table Understanding with LLMs and Entity-Oriented Search
We introduce an entity-oriented search method to enhance table understanding in LLMs, reducing preprocessing and achieving state-of-the-art results.
Our work addresses the challenges of understanding tables. Existing methods often struggle with the unpredictable nature of table content, leading to a reliance on preprocessing and keyword matching. They also face limitations due to the lack of contextual information, which complicates the reasoning processes of large...
[ "Thi-Nhung Nguyen", "Hoang Ngo", "Dinh Phung", "Thuy-Trang Vu", "Dat Quoc Nguyen" ]
https://openreview.net/forum?id=vlyl9xZVAL
vlyl9xZVAL
vlyl9xZVAL
[ "~Thi-Nhung_Nguyen1", "~Hoang_Ngo1", "~Dinh_Phung2", "~Thuy-Trang_Vu1", "~Dat_Quoc_Nguyen1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/68932df0b9be8315ff4155a7db786fcffd032e2b.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "table understanding", "llm" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ nguyen2025improving, title={Improving Table Understanding with {LLM}s and Entity-Oriented Search}, author={Thi-Nhung Nguyen and Hoang Ngo and Dinh Phung and Thuy-Trang Vu and Dat Quoc Nguyen}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=vlyl9xZVA...
nguyen|improving_table_understanding_with_llms_and_entityoriented_search
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Positional Biases Shift as Inputs Approach Context Window Limits
This paper examines how input length, relative to a model’s context window, affects positional biases in LLMs.
Large Language Models (LLMs) often struggle to use information across long inputs effectively. Prior work has identified positional biases, such as the Lost in the Middle (LiM) effect, where models perform better when information appears at the beginning (primacy bias) or end (recency bias) of the input, rather than i...
[ "Blerta Veseli", "Julian Chibane", "Mariya Toneva", "Alexander Koller" ]
https://openreview.net/forum?id=vlUk8z8LaM
vlUk8z8LaM
vlUk8z8LaM
[ "~Blerta_Veseli1", "~Julian_Chibane1", "~Mariya_Toneva1", "~Alexander_Koller2" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/1fa87a52bb87f4535aa4c24f858f215c6d329083.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Long-context understanding", "positional biases" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ veseli2025positional, title={Positional Biases Shift as Inputs Approach Context Window Limits}, author={Blerta Veseli and Julian Chibane and Mariya Toneva and Alexander Koller}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=vlUk8z8LaM} }
veseli|positional_biases_shift_as_inputs_approach_context_window_limits
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Agree to Disagree? A Meta-Evaluation of LLM Misgendering
We conduct a systematic meta-evaluation of different methods for measuring LLM misgendering across three datasets and find that they can disagree.
Numerous methods have been proposed to measure LLM misgendering, including probability-based evaluations (e.g., automatically with templatic sentences) and generation-based evaluations (e.g., with automatic heuristics or human validation). However, it has gone unexamined whether these evaluation methods have convergent...
[ "Arjun Subramonian", "Vagrant Gautam", "Preethi Seshadri", "Dietrich Klakow", "Kai-Wei Chang", "Yizhou Sun" ]
https://openreview.net/forum?id=vgmiRvpCLA
vgmiRvpCLA
vgmiRvpCLA
[ "~Arjun_Subramonian1", "~Vagrant_Gautam1", "~Preethi_Seshadri2", "~Dietrich_Klakow1", "~Kai-Wei_Chang1", "~Yizhou_Sun1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/26bde4c0897c899d2a27271ba979a953201f4f50.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "fairness", "meta-evaluation", "misgendering" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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null
@inproceedings{ subramonian2025agree, title={Agree to Disagree? A Meta-Evaluation of {LLM} Misgendering}, author={Arjun Subramonian and Vagrant Gautam and Preethi Seshadri and Dietrich Klakow and Kai-Wei Chang and Yizhou Sun}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/...
subramonian|agree_to_disagree_a_metaevaluation_of_llm_misgendering
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Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate
We introduce Critique Fine-Tuning, a training method that teaches LM to critique responses, achieving better performance than SFT with fewer training samples and comparable results to RL methods.
Supervised Fine-Tuning (SFT) is commonly used to train language models to imitate annotated responses for given instructions. In this paper, we propose Critique Fine-Tuning (CFT), a method more effective than SFT for reasoning tasks. Instead of simply imitating correct responses, CFT trains models to critique noisy res...
[ "Yubo Wang", "Xiang Yue", "Wenhu Chen" ]
https://openreview.net/forum?id=vTAz44GgOA
vTAz44GgOA
vTAz44GgOA
[ "~Yubo_Wang9", "~Xiang_Yue1", "~Wenhu_Chen3" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/dc57be85188955311eeeb6b9cad46b5469c6b35e.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Reasoning", "Large Language Model", "Fine-Tuning" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ wang2025critique, title={Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate}, author={Yubo Wang and Xiang Yue and Wenhu Chen}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=vTAz44GgOA} }
wang|critique_finetuning_learning_to_critique_is_more_effective_than_learning_to_imitate
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SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild
The paper explores zero training with rule-based rewards for emergent chain-of-thought reasoning in smaller models, producing significant improvements in both reasoning accuracy and CoT length across all settings.
DeepSeek-R1 has shown that long chain-of-thought (CoT) reasoning can naturally emerge through a simple reinforcement learning (RL) framework with rule-based rewards, where the training may directly start from the base models—a paradigm referred to as zero RL training. Most recent efforts to reproduce zero RL training h...
[ "Weihao Zeng", "Yuzhen Huang", "Qian Liu", "Wei Liu", "Keqing He", "Zejun MA", "Junxian He" ]
https://openreview.net/forum?id=vSMCBUgrQj
vSMCBUgrQj
vSMCBUgrQj
[ "~Weihao_Zeng2", "~Yuzhen_Huang2", "~Qian_Liu2", "~Wei_Liu25", "~Keqing_He1", "~Zejun_MA1", "~Junxian_He1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/bbe20558d2168bcb2992d581750387291d026a34.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Reasoning", "Large Language Model" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ zeng2025simplerlzoo, title={Simple{RL}-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild}, author={Weihao Zeng and Yuzhen Huang and Qian Liu and Wei Liu and Keqing He and Zejun MA and Junxian He}, booktitle={Second Conference on Language Modeling}, year={2025}, u...
zeng|simplerlzoo_investigating_and_taming_zero_reinforcement_learning_for_open_base_models_in_the_wild
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Do Large Language Models Have a Planning Theory of Mind? Evidence from MindGames: a Multi-Step Persuasion Task
Humans significantly outperform LLMs at our complex theory of mind task
Recent evidence suggests Large Language Models (LLMs) display Theory of Mind (ToM) abilities. Most ToM experiments place participants in a spectatorial role, wherein they predict and interpret other agents' behavior. However, human ToM also contributes to dynamically planning action and strategically intervening on oth...
[ "Jared Moore", "Ned Cooper", "Rasmus Overmark", "Beba Cibralic", "Cameron Robert Jones", "Nick Haber" ]
https://openreview.net/forum?id=vNJbDhgrM4
vNJbDhgrM4
vNJbDhgrM4
[ "~Jared_Moore1", "~Ned_Cooper1", "~Rasmus_Overmark1", "~Beba_Cibralic1", "~Cameron_Robert_Jones1", "~Nick_Haber1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/5e63f274f962ce2282ab6b0841a50e9c17d911ec.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "theory of mind", "planning", "causal model", "persuasion" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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null
@inproceedings{ moore2025do, title={Do Large Language Models Have a Planning Theory of Mind? Evidence from MindGames: a Multi-Step Persuasion Task}, author={Jared Moore and Ned Cooper and Rasmus Overmark and Beba Cibralic and Cameron Robert Jones and Nick Haber}, booktitle={Second Conference on Language Modeling}, year...
moore|do_large_language_models_have_a_planning_theory_of_mind_evidence_from_mindgames_a_multistep_persuasion_task
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Do Biased Models Have Biased Thoughts?
This paper explores whether biased language models have biased reasoning, finding that their thought processes are not strongly linked to biased outputs.
The impressive performance of language models is undeniable. However, the presence of biases based on gender, race, socio-economic status, physical appearance, and sexual orientation makes the deployment of language models challenging. This paper studies the effect of chain-of-thought prompting, a recent approach that ...
[ "Swati Rajwal", "Shivank Garg", "Reem Abdel-Salam", "Abdelrahman Zayed" ]
https://openreview.net/forum?id=vDr0RV3590
vDr0RV3590
vDr0RV3590
[ "~Swati_Rajwal2", "~Shivank_Garg1", "~Reem_Abdel-Salam1", "~Abdelrahman_Zayed1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/ded6a3254f5a138cc7200d82253bd49853ededb2.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Bias in language models", "Large Language Models", "biased thoughts", "Chain-of-Thought prompting" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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null
@inproceedings{ rajwal2025do, title={Do Biased Models Have Biased Thoughts?}, author={Swati Rajwal and Shivank Garg and Reem Abdel-Salam and Abdelrahman Zayed}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=vDr0RV3590} }
rajwal|do_biased_models_have_biased_thoughts
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How do language models learn facts? Dynamics, curricula and hallucinations
We analyze learning dynamics of language models on a synthetic memory task and show that they learn sequentially, that some data distribution properties lead to faster learning, and that hallucinations appear simulataneously to knowledge acquisition.
Large language models accumulate vast amounts of knowledge during their pre-training, yet the dynamics governing this acquisition remain poorly understood. This work investigates the learning dynamics of language models on a synthetic factual recall task, uncovering three key findings: First, language models learn in t...
[ "Nicolas Zucchet", "Jorg Bornschein", "Stephanie C.Y. Chan", "Andrew Kyle Lampinen", "Razvan Pascanu", "Soham De" ]
https://openreview.net/forum?id=vBcGnragkr
vBcGnragkr
vBcGnragkr
[ "~Nicolas_Zucchet1", "~Jorg_Bornschein1", "~Stephanie_C.Y._Chan1", "~Andrew_Kyle_Lampinen1", "~Razvan_Pascanu1", "~Soham_De2" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/462209e2d72b398c2da64f53207c0eeea6740b0d.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "learning dynamics", "factual recall", "curricula", "data distribution", "hallucinations" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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null
@inproceedings{ zucchet2025how, title={How do language models learn facts? Dynamics, curricula and hallucinations}, author={Nicolas Zucchet and Jorg Bornschein and Stephanie C.Y. Chan and Andrew Kyle Lampinen and Razvan Pascanu and Soham De}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https:/...
zucchet|how_do_language_models_learn_facts_dynamics_curricula_and_hallucinations
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News is More than a Collection of Facts: Moral Frame Preserving News Summarization
The first investigation in how LLMs can summarize news articles while preserving moral framing.
News articles are more than collections of facts; they reflect journalists' framing, shaping how events are presented to the audience. One key aspect of framing is the choice to write in (or quote verbatim) morally charged language as opposed to using neutral terms. This moral framing carries implicit judgments that au...
[ "Enrico Liscio", "Michela Lorandi", "Pradeep K. Murukannaiah" ]
https://openreview.net/forum?id=uzauWUW9u3
uzauWUW9u3
uzauWUW9u3
[ "~Enrico_Liscio1", "~Michela_Lorandi1", "~Pradeep_K._Murukannaiah1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/6940325f5f0ed453a99c0b091dcc120b38f1a6f4.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "LLMs", "news", "summarization", "morality", "framing" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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null
@inproceedings{ liscio2025news, title={News is More than a Collection of Facts: Moral Frame Preserving News Summarization}, author={Enrico Liscio and Michela Lorandi and Pradeep K. Murukannaiah}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=uzauWUW9u3} }
liscio|news_is_more_than_a_collection_of_facts_moral_frame_preserving_news_summarization
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Pairwise or Pointwise? Evaluating Feedback Protocols for Bias in LLM-Based Evaluation
This work examines how feedback protocols (absolute scores vs. pairwise preferences) impact biases in LLM evaluations, revealing that absolute scoring is more robust to distractor features.
Large Language Models (LLMs) are widely used as proxies for human labelers in both training (Reinforcement Learning from AI Feedback) and large-scale response evaluation (LLM-as-a-judge). Alignment and evaluation are critical components in the development of reliable LLMs, and the choice of feedback protocol plays a ce...
[ "Tuhina Tripathi", "Manya Wadhwa", "Greg Durrett", "Scott Niekum" ]
https://openreview.net/forum?id=uyX5Vnow3U
uyX5Vnow3U
uyX5Vnow3U
[ "~Tuhina_Tripathi1", "~Manya_Wadhwa1", "~Greg_Durrett1", "~Scott_Niekum1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/ac5ede1c4f3e38efcb8223c7e17ba3fb8949b2ee.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "evaluation", "data", "alignment" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ tripathi2025pairwise, title={Pairwise or Pointwise? Evaluating Feedback Protocols for Bias in {LLM}-Based Evaluation}, author={Tuhina Tripathi and Manya Wadhwa and Greg Durrett and Scott Niekum}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=uyX5Vn...
tripathi|pairwise_or_pointwise_evaluating_feedback_protocols_for_bias_in_llmbased_evaluation
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Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document Summarization
We present a novel method to estimate optimal context length for retrieval-augmented generation. Our estimate is a function of the retriever, summarizer and the downstream task.
Recent advances in long-context reasoning abilities of language models led to interesting applications in large-scale multi-document summarization. However, prior work has shown that these long-context models are not effective at their claimed context windows. To this end, retrieval-augmented systems provide an efficie...
[ "Adithya Pratapa", "Teruko Mitamura" ]
https://openreview.net/forum?id=uh0Sf8yN7n
uh0Sf8yN7n
uh0Sf8yN7n
[ "~Adithya_Pratapa1", "~Teruko_Mitamura1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/4e0303ac5eb0858029dfdcaa571f784512a0ccdf.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "retrieval-augmented generation", "long-context", "multi-document summarization" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ pratapa2025estimating, title={Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document Summarization}, author={Adithya Pratapa and Teruko Mitamura}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=uh0Sf8yN7n} }
pratapa|estimating_optimal_context_length_for_hybrid_retrievalaugmented_multidocument_summarization
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Missing Premise exacerbates Overthinking: Are Reasoning Models losing Critical Thinking Skill?
Reasoning models can’t think critically when premise is missing.
We find that the response length of reasoning LLMs, whether trained by reinforcement learning or supervised learning, drastically increases for ill-posed questions with missing premises (MiP), ending up with redundant and ineffective thinking. Such failures are against the ``test-time scaling law'' but have been w...
[ "Chenrui Fan", "Ming Li", "Lichao Sun", "Tianyi Zhou" ]
https://openreview.net/forum?id=ufozo2Wc9e
ufozo2Wc9e
ufozo2Wc9e
[ "~Chenrui_Fan1", "~Ming_Li18", "~Lichao_Sun1", "~Tianyi_Zhou2" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/018bea3ab55d1c1e4c93295a221d9e746fe84514.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "LLM", "Reasoning Model", "Overthinking", "Abstain" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ fan2025missing, title={Missing Premise exacerbates Overthinking: Are Reasoning Models losing Critical Thinking Skill?}, author={Chenrui Fan and Ming Li and Lichao Sun and Tianyi Zhou}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net/forum?id=ufozo2Wc9e} }
fan|missing_premise_exacerbates_overthinking_are_reasoning_models_losing_critical_thinking_skill
/attachment/825254c1747951fd04bf0c9067828ba4da07f36f.zip
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Brains vs. Bytes: Evaluating LLM Proficiency in Olympiad Mathematics
We evaluate large language models on Olympiad-level mathematics, revealing their inability to produce rigorous and logically sound proofs despite occasional correct final answers.
Recent advancements in large language models (LLMs) have shown impressive progress in mathematical reasoning tasks. However, current evaluation benchmarks predominantly focus on the accuracy of final answers, often overlooking the logical rigor crucial for mathematical problem-solving. The claim that state-of-the-art L...
[ "Hamed Mahdavi", "Alireza Hashemi", "Majid Daliri", "Pegah Mohammadipour", "Alireza Farhadi", "Samira Malek", "Yekta Yazdanifard", "Amir Khasahmadi", "Vasant G Honavar" ]
https://openreview.net/forum?id=uXR2KsA4L9
uXR2KsA4L9
uXR2KsA4L9
[ "~Hamed_Mahdavi1", "~Alireza_Hashemi2", "~Majid_Daliri1", "~Pegah_Mohammadipour1", "~Alireza_Farhadi2", "~Samira_Malek1", "~Yekta_Yazdanifard1", "~Amir_Khasahmadi1", "~Vasant_G_Honavar1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/d53d2675aef66e061647aa4edbe85fe1f4104521.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Mathematical Reasoning", "Human Evaluation", "Reasoning Evaluation", "Math Problem-Solving" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ mahdavi2025brains, title={Brains vs. Bytes: Evaluating {LLM} Proficiency in Olympiad Mathematics}, author={Hamed Mahdavi and Alireza Hashemi and Majid Daliri and Pegah Mohammadipour and Alireza Farhadi and Samira Malek and Yekta Yazdanifard and Amir Khasahmadi and Vasant G Honavar}, booktitle={Second Co...
mahdavi|brains_vs_bytes_evaluating_llm_proficiency_in_olympiad_mathematics
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BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity
We propose BlockFFN, an effective MoE architecture more friendly for end-side acceleration, as well as its sparsity-aware training objectives and efficient acceleration kernels.
To alleviate the computational burden of large language models (LLMs), architectures with activation sparsity, represented by mixture-of-experts (MoE), have attracted increasing attention. However, the non-differentiable and inflexible routing of vanilla MoE hurts model performance. Moreover, while each token activates...
[ "Chenyang Song", "Weilin Zhao", "Xu Han", "Chaojun Xiao", "Yingfa Chen", "Yuxuan Li", "Zhiyuan Liu", "Maosong Sun" ]
https://openreview.net/forum?id=uLl7tSUOir
uLl7tSUOir
uLl7tSUOir
[ "~Chenyang_Song1", "~Weilin_Zhao1", "~Xu_Han2", "~Chaojun_Xiao1", "~Yingfa_Chen1", "~Yuxuan_Li19", "~Zhiyuan_Liu1", "~Maosong_Sun1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/894e65b966a485306b765993e6715a25060f8737.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "mixture-of-experts", "activation sparsity", "inference acceleration" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ song2025blockffn, title={Block{FFN}: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity}, author={Chenyang Song and Weilin Zhao and Xu Han and Chaojun Xiao and Yingfa Chen and Yuxuan Li and Zhiyuan Liu and Maosong Sun}, booktitle={Second Conference on Language...
song|blockffn_towards_endside_accelerationfriendly_mixtureofexperts_with_chunklevel_activation_sparsity
/attachment/64657ad2b7008be33d41cfe2edd34b44a1e24875.zip
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ProsodyLM: Uncovering the Emerging Prosody Processing Capabilities in Speech Language Models
We propose ProsodyLM, a speech language model that demonstrate impressive emerging prosody generation and understand capabilities simply through pre-training on 30k audiobooks.
Speech language models refer to language models with speech processing and understanding capabilities. One key desirable capability for speech language models is the ability to capture the intricate interdependency between content and prosody. The existing mainstream paradigm of training speech language models, which c...
[ "Kaizhi Qian", "Xulin Fan", "Junrui Ni", "Slava Shechtman", "Mark A. Hasegawa-Johnson", "Chuang Gan", "Yang Zhang" ]
https://openreview.net/forum?id=uBg8PClMUu
uBg8PClMUu
uBg8PClMUu
[ "~Kaizhi_Qian1", "~Xulin_Fan1", "~Junrui_Ni1", "~Slava_Shechtman1", "~Mark_A._Hasegawa-Johnson1", "~Chuang_Gan1", "~Yang_Zhang3" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/6a359ebba5a3de8baa31631883a6b21c9cbc97a3.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Speech LM: Multi-modal LLM" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ qian2025prosodylm, title={Prosody{LM}: Uncovering the Emerging Prosody Processing Capabilities in Speech Language Models}, author={Kaizhi Qian and Xulin Fan and Junrui Ni and Slava Shechtman and Mark A. Hasegawa-Johnson and Chuang Gan and Yang Zhang}, booktitle={Second Conference on Language Modeling}, ...
qian|prosodylm_uncovering_the_emerging_prosody_processing_capabilities_in_speech_language_models
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VaPR - Vision-language Preference alignment for Reasoning
VaPR, a hard-negative preference dataset that mitigates stylistic and length biases in AI feedback, enabling improved reasoning and robustness in preference finetuned (DPO) vision-language models across ten benchmarks.
Preference finetuning methods like Direct Preference Optimization (DPO) with AI-generated feedback have shown promise in aligning Large Vision-Language Models (LVLMs) with human preferences. However, existing techniques overlook the prevalence of noise in synthetic preference annotations in the form of stylistic and le...
[ "Rohan Wadhawan", "Fabrice Y Harel-Canada", "Zi-Yi Dou", "Suhaila Shakiah", "Robinson Piramuthu", "Nanyun Peng" ]
https://openreview.net/forum?id=uBAubFwymy
uBAubFwymy
uBAubFwymy
[ "~Rohan_Wadhawan1", "~Fabrice_Y_Harel-Canada1", "~Zi-Yi_Dou1", "~Suhaila_Shakiah1", "~Robinson_Piramuthu1", "~Nanyun_Peng1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/70b7995114502f1826a2a3c5a3f55008f8261306.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Vision Language Models", "Preference Optimization", "DPO", "Data Generation", "Reasoning" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ wadhawan2025vapr, title={Va{PR} - Vision-language Preference alignment for Reasoning}, author={Rohan Wadhawan and Fabrice Y Harel-Canada and Zi-Yi Dou and Suhaila Shakiah and Robinson Piramuthu and Nanyun Peng}, booktitle={Second Conference on Language Modeling}, year={2025}, url={https://openreview.net...
wadhawan|vapr_visionlanguage_preference_alignment_for_reasoning
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DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning
DeepRetrieval trains query generation models through reinforcement learning instead of supervised data, achieving state-of-the-art performance across diverse retrieval tasks while being more efficient than existing approaches.
Information retrieval systems are crucial for enabling effective access to large document collections. Recent approaches have leveraged Large Language Models (LLMs) to enhance retrieval performance through query augmentation, but often rely on expensive supervised learning or distillation techniques that require signif...
[ "Pengcheng Jiang", "Jiacheng Lin", "Lang Cao", "Runchu Tian", "SeongKu Kang", "Zifeng Wang", "Jimeng Sun", "Jiawei Han" ]
https://openreview.net/forum?id=u9JXu4L17I
u9JXu4L17I
u9JXu4L17I
[ "~Pengcheng_Jiang2", "~Jiacheng_Lin3", "~Lang_Cao2", "~Runchu_Tian1", "~SeongKu_Kang1", "~Zifeng_Wang3", "~Jimeng_Sun3", "~Jiawei_Han1" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/d6e93b6b5f6f01c6793b460b37410d7d7ec3a1cd.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Large Language Models", "Information Retrieval", "Reinforcement Learning" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ jiang2025deepretrieval, title={DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning}, author={Pengcheng Jiang and Jiacheng Lin and Lang Cao and Runchu Tian and SeongKu Kang and Zifeng Wang and Jimeng Sun and Jiawei Han}, booktitle={Second Confer...
jiang|deepretrieval_hacking_real_search_engines_and_retrievers_with_large_language_models_via_reinforcement_learning
/attachment/e1c13f35faae5175020c5266f60439cdda5f1f8f.zip
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SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression
SecurityLingua defends LLMs from jailbreak attacks using secutriy-aware prompt compression to extract the true intention. It helps the model activate its safety guardrails without altering the original prompt in minimal compute and latency overhead.
Large language models (LLMs) have achieved widespread adoption across numerous applications. However, many LLMs are vulnerable to malicious attacks even after safety alignment. These attacks typically bypass LLMs’ safety guardrails by wrapping the original malicious instructions inside adversarial jailbreaks prompts. P...
[ "Yucheng Li", "Surin Ahn", "Huiqiang Jiang", "Amir H. Abdi", "Yuqing Yang", "Lili Qiu" ]
https://openreview.net/forum?id=tybbSo6wba
tybbSo6wba
tybbSo6wba
[ "~Yucheng_Li5", "~Surin_Ahn1", "~Huiqiang_Jiang2", "~Amir_H._Abdi1", "~Yuqing_Yang1", "~Lili_Qiu3" ]
{ "value": "COLM 2025" }
{ "value": "colmweb.org/COLM/2025/Conference" }
{ "value": "/pdf/d284f5f7d160ec273e8ba2c87857c7a6d07c1e91.pdf" }
conference
colmweb.org/COLM/2025/Conference
2,025
COLM
[ "Jailbreak Attacks Defense", "Prompt Compression" ]
I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html
I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html
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@inproceedings{ li2025securitylingua, title={SecurityLingua: Efficient Defense of {LLM} Jailbreak Attacks via Security-Aware Prompt Compression}, author={Yucheng Li and Surin Ahn and Huiqiang Jiang and Amir H. Abdi and Yuqing Yang and Lili Qiu}, booktitle={Second Conference on Language Modeling}, year={2025}, url={http...
li|securitylingua_efficient_defense_of_llm_jailbreak_attacks_via_securityaware_prompt_compression
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