title stringlengths 21 128 | content_TLDR stringlengths 40 250 | abstract stringlengths 613 2.09k | authors listlengths 1 42 | openreview_url stringlengths 42 42 | id stringlengths 10 10 | forum stringlengths 10 10 | authorids listlengths 1 42 | venue dict | venueid dict | pdf_url dict | invitation stringclasses 1
value | group stringclasses 1
value | venue_name stringclasses 1
value | year int64 2.03k 2.03k | conference stringclasses 1
value | content_keywords listlengths 1 16 | content_code_of_ethics stringclasses 1
value | content_author_guide stringclasses 1
value | content_flagged_for_ethics_review bool 1
class | content_ethics_comments stringclasses 11
values | content__bibtex stringlengths 246 1.01k | content_paperhash stringlengths 29 134 | content_supplementary_material stringclasses 73
values | content_award_nomination bool 1
class | content_reciprocal_reviewing_status stringclasses 1
value | content_reciprocal_reviewing_author stringclasses 4
values | content_reciprocal_reviewing_exemption_reason dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Don’t lie to your friends: Learning what you know from collaborative self-play | Task-level reward in a multi-agent collaborative game can teach the agents about calibration and selective tool use | To be helpful assistants, AI agents must be aware of their own capabilities and limitations. This includes knowing when to answer from parametric knowledge versus using tools, when to trust tool outputs, and when to abstain or hedge. Such capabilities are hard to teach through supervised fine-tuning because they requir... | [
"Jacob Eisenstein",
"Reza Aghajani",
"Adam Fisch",
"Dheeru Dua",
"Fantine Huot",
"Mirella Lapata",
"Vicky Zayats",
"Jonathan Berant"
] | https://openreview.net/forum?id=2vDJiGUfhV | 2vDJiGUfhV | 2vDJiGUfhV | [
"~Jacob_Eisenstein1",
"~Reza_Aghajani2",
"~Adam_Fisch2",
"~Dheeru_Dua2",
"~Fantine_Huot1",
"~Mirella_Lapata1",
"~Vicky_Zayats1",
"~Jonathan_Berant1"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/33b60b49c794c267262ad17bde96770ae321292d.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"self-play",
"calibration",
"tool use",
"multiagent"
] | 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{
eisenstein2025dont,
title={Don{\textquoteright}t lie to your friends: Learning what you know from collaborative self-play},
author={Jacob Eisenstein and Reza Aghajani and Adam Fisch and Dheeru Dua and Fantine Huot and Mirella Lapata and Vicky Zayats and Jonathan Berant},
booktitle={Second Conference on ... | eisenstein|dont_lie_to_your_friends_learning_what_you_know_from_collaborative_selfplay | null | true | null | null | null | |
RepoST: Scalable Repository-Level Coding Environment Construction with Sandbox Testing | Automatically constructing repo-level execution-based environments for training and evaluation. | We present RepoST, a scalable method to construct environments that provide execution feedback for repository-level code generation for both training and evaluation. Unlike existing works that aim to build entire repositories for execution, which is challenging for both human and LLMs, we provide execution feedback wit... | [
"Yiqing Xie",
"Alex Xie",
"Divyanshu Sheth",
"Pengfei Liu",
"Daniel Fried",
"Carolyn Rose"
] | https://openreview.net/forum?id=2txrMBpw3q | 2txrMBpw3q | 2txrMBpw3q | [
"~Yiqing_Xie1",
"~Alex_Xie1",
"~Divyanshu_Sheth1",
"~Pengfei_Liu1",
"~Daniel_Fried1",
"~Carolyn_Rose1"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/0f0642854eb629ef6d848738749e7fc2ffc1b800.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"code generation training",
"repo-level 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 | null | null | @inproceedings{
xie2025repost,
title={Repo{ST}: Scalable Repository-Level Coding Environment Construction with Sandbox Testing},
author={Yiqing Xie and Alex Xie and Divyanshu Sheth and Pengfei Liu and Daniel Fried and Carolyn Rose},
booktitle={Second Conference on Language Modeling},
year={2025},
url={https://openrevie... | xie|repost_scalable_repositorylevel_coding_environment_construction_with_sandbox_testing | /attachment/bc410e83bf051f2b0b987d2ac6fc926e03c3548d.zip | null | null | null | null | |
2 OLMo 2 Furious (COLM’s Version) | We release AnonModel, a family of fully open 7B, 13B and 32B models achieving competitive performance with fewer computational cost while providing transparency through released training data, code, checkpoints, and more. | We present OLMo 2, the next generation of our fully open language models. OLMo 2 includes a family of dense autoregressive language models at 7B, 13B, and 32B scales with fully released artifacts—model weights, full training data, training code and recipes, training logs, and thousands of intermediate checkpoints. In t... | [
"Evan Pete Walsh",
"Luca Soldaini",
"Dirk Groeneveld",
"Kyle Lo",
"Shane Arora",
"Akshita Bhagia",
"Yuling Gu",
"Shengyi Huang",
"Matt Jordan",
"Nathan Lambert",
"Dustin Schwenk",
"Oyvind Tafjord",
"Taira Anderson",
"David Atkinson",
"Faeze Brahman",
"Christopher Clark",
"Pradeep Das... | https://openreview.net/forum?id=2ezugTT9kU | 2ezugTT9kU | 2ezugTT9kU | [
"~Evan_Pete_Walsh1",
"~Luca_Soldaini1",
"~Dirk_Groeneveld1",
"~Kyle_Lo1",
"~Shane_Arora1",
"~Akshita_Bhagia1",
"~Yuling_Gu1",
"~Shengyi_Huang1",
"~Matt_Jordan1",
"~Nathan_Lambert1",
"~Dustin_Schwenk1",
"~Oyvind_Tafjord2",
"~Taira_Anderson1",
"~David_Atkinson3",
"~Faeze_Brahman1",
"~Chr... | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/ee2c137da42a7d7cd97b58127c3b38b1bd47107d.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"language model",
"pretraining",
"training stability",
"training data",
"instruction 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 | null | null | @inproceedings{
walsh2025,
title={2 {OLM}o 2 Furious ({COLM}{\textquoteright}s Version)},
author={Evan Pete Walsh and Luca Soldaini and Dirk Groeneveld and Kyle Lo and Shane Arora and Akshita Bhagia and Yuling Gu and Shengyi Huang and Matt Jordan and Nathan Lambert and Dustin Schwenk and Oyvind Tafjord and Taira Anders... | walsh|2_olmo_2_furious_colms_version | null | null | This submission is NOT exempt from the Reciprocal Reviewing requirement. (We expect most submissions to fall in this category.) | ~Kyle_Lo1 | {
"readers": [
"colmweb.org/COLM/2025/Conference",
"colmweb.org/COLM/2025/Conference/Submission1136/Authors"
]
} | |
SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning | TL;DR: We propose SUV, a selective unlearning framework that excises memorized copyrighted content from LLMs without compromising their overall performance. | Large Language Models (LLMs) have transformed natural language processing by learning from massive datasets, yet this rapid progress has also drawn legal scrutiny, as the ability to unintentionally generate copyrighted content has already prompted several prominent lawsuits. In this work, we introduce SUV (Selective Un... | [
"Tianyang Xu",
"Xiaoze Liu",
"Feijie Wu",
"Xiaoqian Wang",
"Jing Gao"
] | https://openreview.net/forum?id=2YdSsi0bxK | 2YdSsi0bxK | 2YdSsi0bxK | [
"~Tianyang_Xu3",
"~Xiaoze_Liu1",
"~Feijie_Wu1",
"~Xiaoqian_Wang1",
"~Jing_Gao1"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/23716f5cef278592db52547b21cc4ce2972d69ed.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"LLMs; Calibration; Copyright; Unlearning"
] | 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{
xu2025suv,
title={{SUV}: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning},
author={Tianyang Xu and Xiaoze Liu and Feijie Wu and Xiaoqian Wang and Jing Gao},
booktitle={Second Conference on Language Modeling},
year={2025},
url={https://openreview.net/forum?id=2YdS... | xu|suv_scalable_large_language_model_copyright_compliance_with_regularized_selective_unlearning | null | null | null | null | null | |
PredGen: Accelerated Inference of Large Language Models through Input-Time Speculation for Real-Time Speech Interaction | We leverage speculative decoding at user input time to reduce the latency of speech interactions. | Large Language Models (LLMs) are widely used in real-time voice chat applications, typically in combination with text-to-speech (TTS) systems to generate audio responses. However, their large size often leads to noticeable latency between the end of user input and the start of audio output, resulting in suboptimal user... | [
"Shufan Li",
"Aditya Grover"
] | https://openreview.net/forum?id=2Kl8Ztw6wk | 2Kl8Ztw6wk | 2Kl8Ztw6wk | [
"~Shufan_Li1",
"~Aditya_Grover1"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/9999eac5fd953cddbee91e9c9d9168d017c657f5.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"Large Language Models",
"Inference",
"Speculative Decoding"
] | 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{
li2025predgen,
title={PredGen: Accelerated Inference of Large Language Models through Input-Time Speculation for Real-Time Speech Interaction},
author={Shufan Li and Aditya Grover},
booktitle={Second Conference on Language Modeling},
year={2025},
url={https://openreview.net/forum?id=2Kl8Ztw6wk}
} | li|predgen_accelerated_inference_of_large_language_models_through_inputtime_speculation_for_realtime_speech_interaction | null | null | null | null | null | |
Language models align with brain regions that represent concepts across modalities | We find that LMs predict brain signal better in areas that respond more consistently to the same concept encoded in different modalities. | Cognitive science and neuroscience have long faced the challenge of disentangling representations of language from representations of conceptual meaning. As the same problem arises in today's language models (LMs), we investigate the relationship between LM--brain alignment and two neural metrics: (1) the level of brai... | [
"Maria Ryskina",
"Greta Tuckute",
"Alexander Fung",
"Ashley Malkin",
"Evelina Fedorenko"
] | https://openreview.net/forum?id=2JohTFaGbW | 2JohTFaGbW | 2JohTFaGbW | [
"~Maria_Ryskina1",
"~Greta_Tuckute1",
"~Alexander_Fung1",
"~Ashley_Malkin1",
"~Evelina_Fedorenko1"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/429ed5a7923c63dc595d8547a5686bc7ac1fc9bd.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"LM–brain alignment",
"fMRI",
"conceptual meaning",
"cognitive neuroscience"
] | 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{
ryskina2025language,
title={Language models align with brain regions that represent concepts across modalities},
author={Maria Ryskina and Greta Tuckute and Alexander Fung and Ashley Malkin and Evelina Fedorenko},
booktitle={Second Conference on Language Modeling},
year={2025},
url={https://openreview.n... | ryskina|language_models_align_with_brain_regions_that_represent_concepts_across_modalities | null | null | null | null | null | |
Truth-value judgment in language models: ‘truth directions’ are context sensitive | Investigation of the in-context behaviour of LLM 'truth directions'. | Recent work has demonstrated that the latent spaces of large language models (LLMs) contain directions predictive of the truth of sentences. Multiple methods recover such directions and build probes that are described as uncovering a model’s “knowledge” or “beliefs”. We investigate this phenomenon, looking closely at t... | [
"Stefan F. Schouten",
"Peter Bloem",
"Ilia Markov",
"Piek Vossen"
] | https://openreview.net/forum?id=2H85485yAb | 2H85485yAb | 2H85485yAb | [
"~Stefan_F._Schouten1",
"~Peter_Bloem1",
"~Ilia_Markov2",
"~Piek_Vossen2"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/e6dc394daf825c536dba3787225b727ce1977be1.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"mechinterp",
"mechanistic interpretability",
"interpretability",
"truth directions",
"LLM beliefs",
"large language model",
"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 | null | null | @inproceedings{
schouten2025truthvalue,
title={Truth-value judgment in language models: {\textquoteleft}truth directions{\textquoteright} are context sensitive},
author={Stefan F. Schouten and Peter Bloem and Ilia Markov and Piek Vossen},
booktitle={Second Conference on Language Modeling},
year={2025},
url={https://ope... | schouten|truthvalue_judgment_in_language_models_truth_directions_are_context_sensitive | null | null | null | null | null | |
Context-Adaptive Multi-Prompt Embedding with Large Language Models for Vision-Language Alignment | We propose a method that uses multiple context-adaptive prompts with a pretrained LLM to generate diverse text embeddings for contrastive vision-language learning. | We propose Context-Adaptive Multi-Prompt Embedding, a novel approach to enrich semantic representations in vision-language contrastive learning. Unlike standard CLIP-style models that rely on a single text embedding, our method introduces multiple structured prompts, each containing a distinct adaptive token that captu... | [
"Dahun Kim",
"Anelia Angelova"
] | https://openreview.net/forum?id=29jP6OsrIQ | 29jP6OsrIQ | 29jP6OsrIQ | [
"~Dahun_Kim1",
"~Anelia_Angelova1"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/f81ca9025bba2c317f120f349e32aa1c6814c7b8.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"CLIP",
"contrastive learning",
"LLM embedding"
] | 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{
kim2025contextadaptive,
title={Context-Adaptive Multi-Prompt Embedding with Large Language Models for Vision-Language Alignment},
author={Dahun Kim and Anelia Angelova},
booktitle={Second Conference on Language Modeling},
year={2025},
url={https://openreview.net/forum?id=29jP6OsrIQ}
} | kim|contextadaptive_multiprompt_embedding_with_large_language_models_for_visionlanguage_alignment | /attachment/5bc95b106630bdf9b944809b0efbe6b8eb7c0a1f.zip | null | null | null | null | |
FalseReject: A Resource for Improving Contextual Safety and Mitigating Over-Refusals in LLMs via Structured Reasoning | We introduce FalseReject, a dataset and method to reduce unnecessary refusals in language models, significantly improving their balance between safety and helpfulness. | Safety alignment approaches in large language models (LLMs) often lead to the over-refusal of benign queries, significantly diminishing their utility in sensitive scenarios. To address this challenge, we introduce FalseReject, a comprehensive resource containing 16k seemingly toxic queries accompanied by structured res... | [
"Zhehao Zhang",
"Weijie Xu",
"Fanyou Wu",
"Chandan K. Reddy"
] | https://openreview.net/forum?id=1w9Hay7tvm | 1w9Hay7tvm | 1w9Hay7tvm | [
"~Zhehao_Zhang1",
"~Weijie_Xu1",
"~Fanyou_Wu1",
"~Chandan_K._Reddy1"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/ec166f8ec049c9b30eba1ec7ea72c691f741fb37.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"Over-refusal",
"Safety",
"Instruction Tunning"
] | 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{
zhang2025falsereject,
title={FalseReject: A Resource for Improving Contextual Safety and Mitigating Over-Refusals in {LLM}s via Structured Reasoning},
author={Zhehao Zhang and Weijie Xu and Fanyou Wu and Chandan K. Reddy},
booktitle={Second Conference on Language Modeling},
year={2025},
url={https://ope... | zhang|falsereject_a_resource_for_improving_contextual_safety_and_mitigating_overrefusals_in_llms_via_structured_reasoning | null | null | null | null | null | |
Modifying Large Language Model Post-Training for Diverse Creative Writing | In creative writing generation, we facilitate diversity in LLM outputs by counting in how each training instance differs from other instances with the same prompt. | As creative writing tasks do not have singular correct answers, large language models (LLMs) trained to perform these tasks should be able to generate diverse valid outputs. However, LLM post-training often focuses on improving generation quality but neglects to facilitate output diversity. Hence, in creative writing g... | [
"John Joon Young Chung",
"Vishakh Padmakumar",
"Melissa Roemmele",
"Yuqian Sun",
"Max Kreminski"
] | https://openreview.net/forum?id=1Pmuw08LoM | 1Pmuw08LoM | 1Pmuw08LoM | [
"~John_Joon_Young_Chung1",
"~Vishakh_Padmakumar1",
"~Melissa_Roemmele1",
"~Yuqian_Sun1",
"~Max_Kreminski1"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/46f97e4db02cec3f04f7d04c3fc6e7a599a585af.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"creative writing generation",
"diversity",
"post-training"
] | 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{
chung2025modifying,
title={Modifying Large Language Model Post-Training for Diverse Creative Writing},
author={John Joon Young Chung and Vishakh Padmakumar and Melissa Roemmele and Yuqian Sun and Max Kreminski},
booktitle={Second Conference on Language Modeling},
year={2025},
url={https://openreview.net... | chung|modifying_large_language_model_posttraining_for_diverse_creative_writing | /attachment/4ba9fd43d8499d750dec728005bede64a9ef32bf.zip | null | null | null | null | |
BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning | We propose BigCharts, a diverse chart dataset, and a training framework that improves VLMs’ chart comprehension. | Charts are essential to data analysis, transforming raw data into clear visual representations that support human decision-making.
Although current vision-language models (VLMs) have made significant progress, they continue to struggle with chart comprehension due to training on datasets that lack diversity and real-w... | [
"Ahmed Masry",
"Abhay Puri",
"Masoud Hashemi",
"Juan A. Rodriguez",
"Megh Thakkar",
"Khyati Mahajan",
"Vikas Yadav",
"Sathwik Tejaswi Madhusudhan",
"Alexandre Piché",
"Dzmitry Bahdanau",
"Christopher Pal",
"David Vazquez",
"Enamul Hoque",
"Perouz Taslakian",
"Sai Rajeswar",
"Spandana G... | https://openreview.net/forum?id=19fydz1QnW | 19fydz1QnW | 19fydz1QnW | [
"~Ahmed_Masry1",
"~Abhay_Puri1",
"~Masoud_Hashemi1",
"~Juan_A._Rodriguez1",
"~Megh_Thakkar1",
"~Khyati_Mahajan1",
"~Vikas_Yadav2",
"~Sathwik_Tejaswi_Madhusudhan2",
"~Alexandre_Piché1",
"~Dzmitry_Bahdanau1",
"~Christopher_Pal1",
"~David_Vazquez1",
"~Enamul_Hoque2",
"~Perouz_Taslakian1",
"... | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/1a434bfeb2d15ce12abaf1e3a291e089decb700a.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"charts",
"chartqa",
"vision language models",
"multimodal"
] | 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{
masry2025bigchartsr,
title={BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning},
author={Ahmed Masry and Abhay Puri and Masoud Hashemi and Juan A. Rodriguez and Megh Thakkar and Khyati Mahajan and Vikas Yadav and Sathwik Tejaswi Madhusudhan and Alexandre Pich{\'e} and Dzmitry Ba... | masry|bigchartsr1_enhanced_chart_reasoning_with_visual_reinforcement_finetuning | null | null | null | null | null | |
Noiser: Bounded Input Perturbations for Attributing Large Language Models | We introduce Noiser, a bounded perturbation-based method to explain LLMs predictions and tackle distribution shift. We also propose answerability, a new metric that evaluates the relevance of rationales without gold data or human evaluation. | Feature attribution (FA) methods are common post-hoc approaches that explain how Large Language Models (LLMs) make predictions. Accordingly, generating faithful attributions that reflect the actual inner behavior of the model is crucial. In this paper, we introduce Noiser, a perturbation-based FA method that imposes bo... | [
"Mohammad Reza Ghasemi Madani",
"Aryo Pradipta Gema",
"Yu Zhao",
"Gabriele Sarti",
"Pasquale Minervini",
"Andrea Passerini"
] | https://openreview.net/forum?id=17yFbHmblo | 17yFbHmblo | 17yFbHmblo | [
"~Mohammad_Reza_Ghasemi_Madani1",
"~Aryo_Pradipta_Gema1",
"~Yu_Zhao13",
"~Gabriele_Sarti1",
"~Pasquale_Minervini4",
"~Andrea_Passerini2"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/b3e0eeffcd43001aba85a5e79caa97470f363e5b.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"Feature Attribution",
"Post-hoc Explanations",
"Large Language Model",
"LLMs"
] | 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{
madani2025noiser,
title={Noiser: Bounded Input Perturbations for Attributing Large Language Models},
author={Mohammad Reza Ghasemi Madani and Aryo Pradipta Gema and Yu Zhao and Gabriele Sarti and Pasquale Minervini and Andrea Passerini},
booktitle={Second Conference on Language Modeling},
year={2025},
u... | madani|noiser_bounded_input_perturbations_for_attributing_large_language_models | null | null | null | null | null | |
ALFA: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning | Novel alignment recipe to teach LLMs perform complex goals by (1) decomposing it into more tangible attributes, (2) creating synthetic data, and (3) integrating the attributes. | Large language models (LLMs) often fail to ask effective questions under uncertainty, making them unreliable in domains where proactive information-gathering is essential for decision-making. We present ALignment via Fine-grained Attributes, (ALFA) a framework that improves LLM question-asking by (i) decomposing the no... | [
"Shuyue Stella Li",
"Jimin Mun",
"Faeze Brahman",
"Pedram Hosseini",
"Bryceton G. Thomas",
"Jessica M. Sin",
"Bing Ren",
"Jonathan S. Ilgen",
"Yulia Tsvetkov",
"Maarten Sap"
] | https://openreview.net/forum?id=12u7diwku0 | 12u7diwku0 | 12u7diwku0 | [
"~Shuyue_Stella_Li1",
"~Jimin_Mun1",
"~Faeze_Brahman1",
"~Pedram_Hosseini1",
"~Bryceton_G._Thomas1",
"~Jessica_M._Sin1",
"~Bing_Ren1",
"~Jonathan_S._Ilgen1",
"~Yulia_Tsvetkov1",
"~Maarten_Sap1"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/8083d74723508dd138fb4da403569d14619daf33.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"Information Seeking",
"Question Asking",
"Reliable LLM",
"Clinical Reasoning",
"Structured Rewards"
] | 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{
li2025alfa,
title={{ALFA}: Aligning {LLM}s to Ask Good Questions A Case Study in Clinical Reasoning},
author={Shuyue Stella Li and Jimin Mun and Faeze Brahman and Pedram Hosseini and Bryceton G. Thomas and Jessica M. Sin and Bing Ren and Jonathan S. Ilgen and Yulia Tsvetkov and Maarten Sap},
booktitle={... | li|alfa_aligning_llms_to_ask_good_questions_a_case_study_in_clinical_reasoning | null | null | null | null | null | |
Off-Policy Corrected Reward Modeling for Reinforcement Learning from Human Feedback | We propose to use importance weighting to iteratively retrain an off-policy corrected reward model, resulting in a signficantly better final policy. | Reinforcement Learning from Human Feedback (RLHF) allows us to train models, such as language models (LMs), to follow complex human preferences. In RLHF for LMs, we first train an LM using supervised fine-tuning, sample pairs of responses, obtain human feedback, and use the resulting data to train a reward model (RM). ... | [
"Johannes Ackermann",
"Takashi Ishida",
"Masashi Sugiyama"
] | https://openreview.net/forum?id=0zxugBcgF5 | 0zxugBcgF5 | 0zxugBcgF5 | [
"~Johannes_Ackermann1",
"~Takashi_Ishida1",
"~Masashi_Sugiyama1"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/f388799f7d6563f4c4688c9d4afd2a33cba72370.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"alignment",
"reinforcement learning from human feedback",
"reinforcement learning",
"reward modeling"
] | 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{
ackermann2025offpolicy,
title={Off-Policy Corrected Reward Modeling for Reinforcement Learning from Human Feedback},
author={Johannes Ackermann and Takashi Ishida and Masashi Sugiyama},
booktitle={Second Conference on Language Modeling},
year={2025},
url={https://openreview.net/forum?id=0zxugBcgF5}
} | ackermann|offpolicy_corrected_reward_modeling_for_reinforcement_learning_from_human_feedback | null | null | null | null | null | |
MAC: A Live Benchmark for Multimodal Large Language Models in Scientific Understanding | A Live Benchmark for Multimodal Large Language Models in Scientific Understanding | As multimodal large language models (MLLMs) grow increasingly capable, fixed benchmarks are gradually losing their effectiveness in evaluating high-level scientific understanding. In this paper, we introduce the Multimodal Academic Cover benchmark (MAC), a live benchmark that could continuously evolve with scientific a... | [
"Mohan Jiang",
"Jin Gao",
"Jiahao Zhan",
"Dequan Wang"
] | https://openreview.net/forum?id=0aHOVhkuOB | 0aHOVhkuOB | 0aHOVhkuOB | [
"~Mohan_Jiang1",
"~Jin_Gao3",
"~Jiahao_Zhan1",
"~Dequan_Wang1"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/a1789c20b738bb8892ffc7f3489a13f8d78352e4.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"MLLM",
"Benchmark",
"Scientific Understanding Capabilities"
] | 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{
jiang2025mac,
title={{MAC}: A Live Benchmark for Multimodal Large Language Models in Scientific Understanding},
author={Mohan Jiang and Jin Gao and Jiahao Zhan and Dequan Wang},
booktitle={Second Conference on Language Modeling},
year={2025},
url={https://openreview.net/forum?id=0aHOVhkuOB}
} | jiang|mac_a_live_benchmark_for_multimodal_large_language_models_in_scientific_understanding | /attachment/9e5107c434a576e2bea79b55e38158745b152410.zip | null | null | null | null | |
Impact-driven Context Filtering For Cross-file Code Completion | We introduce an adaptive retrieval-augmented framework for repository-level code completion, which automatically filters irrelevant context to enhance completion accuracy and efficiency. | Retrieval-augmented generation (RAG) has recently demonstrated considerable potential for repository-level code completion, as it integrates cross-file knowledge with in-file preceding code to provide comprehensive contexts for generation. To better understand the contribution of the retrieved cross-file contexts, we i... | [
"Yanzhou Li",
"Shangqing Liu",
"Kangjie Chen",
"Tianwei Zhang",
"Yang Liu"
] | https://openreview.net/forum?id=0Y2zXLFBji | 0Y2zXLFBji | 0Y2zXLFBji | [
"~Yanzhou_Li1",
"~Shangqing_Liu1",
"~Kangjie_Chen1",
"~Tianwei_Zhang1",
"~Yang_Liu36"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/9d676b553f9f39c1dd7d5e1a823ecbbc1e0d6f51.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"Code Completion; Adaptive Retreivla-augmented Generation; 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 | null | null | @inproceedings{
li2025impactdriven,
title={Impact-driven Context Filtering For Cross-file Code Completion},
author={Yanzhou Li and Shangqing Liu and Kangjie Chen and Tianwei Zhang and Yang Liu},
booktitle={Second Conference on Language Modeling},
year={2025},
url={https://openreview.net/forum?id=0Y2zXLFBji}
} | li|impactdriven_context_filtering_for_crossfile_code_completion | null | null | null | null | null | |
Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture | JEPA4Rec improves sequential recommendation by combining joint embedding predictive architecture with language modeling, achieving better item representations and outperforming existing methods, especially in low-resource and cross-domain scenarios. | Language representation learning has emerged as a promising approach for sequential recommendation, thanks to its ability to learn generalizable representations. However, despite its advantages, this approach still struggles with data sparsity and a limited understanding of common-sense user preferences. To address the... | [
"Nguyen Anh Minh",
"Dung D. Le"
] | https://openreview.net/forum?id=0Qbwjd0fxB | 0Qbwjd0fxB | 0Qbwjd0fxB | [
"~Nguyen_Anh_Minh1",
"~Dung_D._Le2"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/15165329da5a76904efdb6e90d529cb9b849bc5f.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"language models",
"joint embedding predictive architecture",
"sequential recommendation"
] | 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{
minh2025learning,
title={Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture},
author={Nguyen Anh Minh and Dung D. Le},
booktitle={Second Conference on Language Modeling},
year={2025},
url={https://openreview.net/forum?id=0Qbwjd0fxB}
} | minh|learning_effective_language_representations_for_sequential_recommendation_via_joint_embedding_predictive_architecture | /attachment/39c271ff9b08d923e1db47f3b6618f8d65046970.zip | null | null | null | null | |
BEARCUBS: A benchmark for computer-using web agents | We introduce BEARCUBS, a benchmark of 111 information-seeking questions designed to evaluate a web agent’s ability to search, browse, and identify factual information from the web. | Modern web agents possess computer use abilities that allow them to interact with webpages by sending commands to a virtual keyboard and mouse. While such agents have considerable potential to assist human users with complex tasks, evaluating their capabilities in real-world settings poses a major challenge. To this en... | [
"Yixiao Song",
"Katherine Thai",
"Chau Minh Pham",
"Yapei Chang",
"Mazin Nadaf",
"Mohit Iyyer"
] | https://openreview.net/forum?id=0JzWiigkUy | 0JzWiigkUy | 0JzWiigkUy | [
"~Yixiao_Song1",
"~Katherine_Thai1",
"~Chau_Minh_Pham1",
"~Yapei_Chang1",
"~Mazin_Nadaf1",
"~Mohit_Iyyer1"
] | {
"value": "COLM 2025"
} | {
"value": "colmweb.org/COLM/2025/Conference"
} | {
"value": "/pdf/0c8e8f33881d200624dd89fc08cb7bc3ef3e7f55.pdf"
} | conference | colmweb.org/COLM/2025/Conference | 2,025 | COLM | [
"computer-use agent",
"benchmark",
"multimodal"
] | 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{
song2025bearcubs,
title={{BEARCUBS}: A benchmark for computer-using web agents},
author={Yixiao Song and Katherine Thai and Chau Minh Pham and Yapei Chang and Mazin Nadaf and Mohit Iyyer},
booktitle={Second Conference on Language Modeling},
year={2025},
url={https://openreview.net/forum?id=0JzWiigkUy}
} | song|bearcubs_a_benchmark_for_computerusing_web_agents | /attachment/a29bd3e2638735810acd3292f411a4a586ca58c3.zip | null | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.