Datasets:
ID large_stringlengths 10 67 | year int64 1.96k 2.03k | title large_stringlengths 4 560 | abstract large_stringlengths 0 12.8k |
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buhnila-etal-2025-chain | 2,025 | Chain-of-MetaWriting: Linguistic and Textual Analysis of How Small Language Models Write Young Students Texts | Large Language Models (LLMs) have been used to generate texts in response to different writing tasks: reports, essays, story telling. However, language models do not have a metarepresentation of the text writing process, nor inherent communication learning needs, comparable to those of young human students. This paper ... |
shi-penn-2025-semantic | 2,025 | Semantic Masking in a Needle-in-a-haystack Test for Evaluating Large Language Model Long-Text Capabilities | In this paper, we introduce the concept of Semantic Masking, where semantically coherent surrounding text (the haystack) interferes with the retrieval and comprehension of specific information (the needle) embedded within it. We propose the Needle-in-a-Haystack-QA Test, an evaluation pipeline that assesses LLMs' long-t... |
khallaf-etal-2025-reading | 2,025 | Reading Between the Lines: A dataset and a study on why some texts are tougher than others | Our research aims at better understanding what makes a text difficult to read for specific audiences with intellectual disabilities, more specifically, people who have limitations in cognitive functioning, such as reading and understanding skills, an IQ below 70, and challenges in conceptual domains. We introduce a sch... |
jourdan-etal-2025-pararev | 2,025 | ParaRev : Building a dataset for Scientific Paragraph Revision annotated with revision instruction | Revision is a crucial step in scientific writing, where authors refine their work to improve clarity, structure, and academic quality. Existing approaches to automated writing assistance often focus on sentence-level revisions, which fail to capture the broader context needed for effective modification. In this paper, ... |
maggi-vitaletti-2025-towards | 2,025 | Towards an operative definition of creative writing: a preliminary assessment of creativeness in AI and human texts | Nowadays, AI is present in all our activities. This pervasive presence is perceived as a threat by many category of users that might be substituted by their AI counterpart. While the potential of AI in handling repetitive tasks is clear, the potentials of its creativeness is still misunderstood. We believe that underst... |
sato-kobayashi-2025-decoding | 2,025 | Decoding Semantic Representations in the Brain Under Language Stimuli with Large Language Models | Brain decoding technology is paving the way for breakthroughs in the interpretation of neural activity to recreate thoughts, emotions, and movements. Tang et al. (2023) introduced a novel approach that uses language models as generative models for brain decoding based on functional magnetic resonance imaging (fMRI) dat... |
drakesmith-etal-2025-towards | 2,025 | Towards a Social Media-based Disease Surveillance System for Early Detection of Influenza-like Illnesses: A Twitter Case Study in Wales | Social media offers the potential to provide detection of outbreaks or public health incidents faster than traditional reporting mechanisms. In this paper, we developed and tested a pipeline to produce alerts of influenza-like illness (ILI) using Twitter data. Data was collected from the Twitter API, querying keywords ... |
du-etal-2025-sentiment | 2,025 | Sentiment Analysis on Video Transcripts: Comparing the Value of Textual and Multimodal Annotations | This study explores the differences between textual and multimodal sentiment annotations on videos and their impact on transcript-based sentiment modelling. Using the UniC and CH-SIMS datasets which are annotated at both the unimodal and multimodal level, we conducted a statistical analysis and sentiment modelling expe... |
shmidman-shmidman-2025-restoring | 2,025 | Restoring Missing Spaces in Scraped Hebrew Social Media | A formidable challenge regarding scraped corpora of social media is the omission of whitespaces, causing pairs of words to be conflated together as one. In order for the text to be properly parsed and analyzed, these missing spaces must be detected and restored. However, it is particularly hard to restore whitespace in... |
juffs-naismith-2025-identifying | 2,025 | Identifying and analyzing \textquoteleftnoisy' spelling errors in a second language corpus | This paper addresses the problem of identifying and analyzing {\textquoteleft}noisy' spelling errors in texts written by second language (L2) learners' texts in a written corpus. Using Python, spelling errors were identified in 5774 texts greater than or equal to 66 words (total=1,814,209 words), selected from a corpus... |
maarouf-tanguy-2025-automatic | 2,025 | Automatic normalization of noisy technical reports with an LLM: What effects on a downstream task? | This study explores the automatic normalization of noisy and highly technical anomaly reports by an LLM. Different prompts are tested to instruct the LLM to clean the text without changing the structure, vocabulary or specialized lexicon. The evaluation of this task is made in two steps. First, the Character Error Rate... |
srivastava-chiang-2025-calling | 2,025 | We`re Calling an Intervention: Exploring Fundamental Hurdles in Adapting Language Models to Nonstandard Text | We present a suite of experiments that allow us to understand the underlying challenges of language model adaptation to nonstandard text. We do so by designing interventions that approximate core features of user-generated text and their interactions with existing biases of language models. Applying our interventions d... |
birkmose-etal-2025-device | 2,025 | On-Device LLMs for Home Assistant: Dual Role in Intent Detection and Response Generation | This paper investigates whether Large Language Models (LLMs), fine-tuned on synthetic but domain-representative data, can perform the twofold task of (i) slot and intent detection and (ii) natural language response generation for a smart home assistant, while running solely on resource-limited, CPU-only edge hardware. ... |
gonzalez-lopez-etal-2025-applying | 2,025 | Applying Transformer Architectures to Detect Cynical Comments in Spanish Social Media | Detecting cynical comments in online communication poses a significant challenge in human-computer interaction, especially given the massive proliferation of discussions on platforms like YouTube. These comments often include offensive or disruptive patterns, such as sarcasm, negative feelings, specific reasons, and an... |
mirbeygi-beigy-2025-prompt | 2,025 | Prompt Guided Diffusion for Controllable Text Generation | Controlled text generation, originally a task to generate coherent, contextually relevant text with specified attributes such as sentiment, topic, or style, has seen a lot of development with methods that use PPLM, FUDGE, and diffusion-based models. However, most state-of-the-art methods balance control precision with ... |
masumi-etal-2025-fabert | 2,025 | FaBERT: Pre-training BERT on Persian Blogs | We introduce FaBERT, a Persian BERT-base model pre-trained on the HmBlogs corpus, encompassing both informal and formal Persian texts. FaBERT is designed to excel in traditional Natural Language Understanding (NLU) tasks, addressing the intricacies of diverse sentence structures and linguistic styles prevalent in the P... |
qian-etal-2025-automatically | 2,025 | Automatically Generating Chinese Homophone Words to Probe Machine Translation Estimation Systems | Evaluating machine translation (MT) of user-generated content (UGC) involves unique challenges such as checking whether the nuance of emotions from the source are preserved in the target text. Recent studies have proposed emotion-related datasets, frameworks and models to automatically evaluate MT quality of Chinese UG... |
abed-azad-beigy-2025-multi | 2,025 | Multi-BERT: Leveraging Adapters for Low-Resource Multi-Domain Adaptation | Multi-domain text analysis presents significant challenges, particularly in Persian name entity recognition (NER). Using a single model for multiple domains often fails to capture the specific features of different domains. That is why many scientists have focused on prompting chatbots for this issue. However, studies ... |
ehsan-solorio-2025-enhancing | 2,025 | Enhancing NER Performance in Low-Resource Pakistani Languages using Cross-Lingual Data Augmentation | Named Entity Recognition (NER), a fundamental task in Natural Language Processing (NLP), has shown significant advancements for high-resource languages. However, due to a lack of annotated datasets and limited representation in Pre-trained Language Models (PLMs), it remains understudied and challenging for low-resource... |
borkakoty-espinosa-anke-2025-wikipedia | 2,025 | Wikipedia is Not a Dictionary, Delete! Text Classification as a Proxy for Analysing Wiki Deletion Discussions | Automated content moderation for collaborative knowledge hubs like Wikipedia or Wikidata is an important yet challenging task due to multiple factors. In this paper, we construct a database of discussions happening around articles marked for deletion in several Wikis and in three languages, which we then use to evaluat... |
znotins-gruzitis-2025-conversational | 2,025 | From Conversational Speech to Readable Text: Post-Processing Noisy Transcripts in a Low-Resource Setting | We present ongoing research on automatic post-processing approaches to enhance the readability of noisy speech transcripts in low-resource languages, with a focus on conversational speech in Latvian. We compare transformer-based sequence-labeling models and large language models (LLMs) for the standard punctuation and ... |
kondo-etal-2025-text | 2,025 | Text Normalization for Japanese Sentiment Analysis | We manually normalize noisy Japanese expressions on social networking services (SNS) to improve the performance of sentiment polarity classification.Despite advances in pre-trained language models, informal expressions found in social media still plague natural language processing.In this study, we analyzed 6,000 posts... |
piper-bagga-2025-narradetect | 2,025 | NarraDetect: An annotated dataset for the task of narrative detection | Narrative detection is an important task across diverse research domains where storytelling serves as a key mechanism for explaining human beliefs and behavior. However, the task faces three significant challenges: (1) inter-narrative heterogeneity, or the variation in narrative communication across social contexts; (2... |
dey-lal-2025-transferability | 2,025 | On the Transferability of Causal Knowledge for Language Models | Language understanding includes identifying logical connections between events in a discourse, such as news and instructional text. We study the transferability of causal knowledge across these two domains by analyzing the extent to which understanding preconditions in narratives such as news articles can help models r... |
shokri-etal-2025-finding | 2,025 | Finding Common Patterns in Domestic Violence Stories Posted on Reddit | Domestic violence survivors often share their experiences in online spaces, offering valuable insights into common abuse patterns. This study analyzes a dataset of personal narratives about domestic violence from Reddit, focusing on event extraction and topic modeling to uncover recurring themes. We evaluate GPT-4 and ... |
bissell-etal-2025-theoretical | 2,025 | A Theoretical Framework for Evaluating Narrative Surprise in Large Language Models | Narrative surprise is a core element of storytelling for engaging audiences, and yet it remains underexplored in the context of large language models (LLMs) and narrative generation. While surprise arises from events that deviate from expectations while maintaining retrospective coherence, current computational approac... |
baruah-narayanan-2025-chatter | 2,025 | CHATTER: A character-attribution dataset for narrative understanding | Computational narrative understanding studies the identification, description, and interaction of the elements of a narrative: characters, attributes, events, and relations.Narrative research has given considerable attention to defining and classifying character types.However, these character-type taxonomies do not gen... |
sancheti-rudinger-2025-tracking | 2,025 | Tracking Evolving Relationship Between Characters in Books in the Era of Large Language Models | This work aims to assess the zero-shot social reasoning capabilities of LLMs by proposing various strategies based on the granularity of information used to track the fine-grained evolution in the relationship between characters in a book. Without gold annotations, we thoroughly analyze the agreements between predictio... |
ghaffari-hokamp-2025-narrative | 2,025 | Narrative Studio: Visual narrative exploration using LLMs and Monte Carlo Tree Search | Interactive storytelling benefits from planning and exploring multiple {\textquotedblleft}what if{\textquotedblright} scenarios. Modern LLMs are useful tools for ideation and exploration, but current chat-based user interfaces restrict users to a single linear flow. To address this limitation, we propose Narrative Stud... |
gobara-etal-2025-speaker | 2,025 | Speaker Identification and Dataset Construction Using LLMs: A Case Study on Japanese Narratives | Speaker identification in narrative analysis is a challenging task due to complex dialogues, diverse utterance patterns, and ambiguous character references. Cosly and time-intensive manual annotation limits the scalability of high-quality dataset creation.This study demonstrates a cost-efficient approach of constructin... |
lamsiyah-etal-2025-arabicsense | 2,025 | ArabicSense: A Benchmark for Evaluating Commonsense Reasoning in Arabic with Large Language Models | Recent efforts in natural language processing (NLP) commonsense reasoning research have led to the development of numerous new datasets and benchmarks. However, these resources have predominantly been limited to English, leaving a gap in evaluating commonsense reasoning in other languages. In this paper, we introduce t... |
hamed-etal-2025-lahjawi | 2,025 | Lahjawi: Arabic Cross-Dialect Translator | In this paper, we explore the rich diversity of Arabic dialects by introducing a suite of pioneering models called Lahjawi. The primary model, Lahjawi-D2D, is the first designed for cross-dialect translation among 15 Arabic dialects. Furthermore, we introduce Lahjawi-D2MSA, a model designed to convert any Arabic dialec... |
bezancon-etal-2025-lost | 2,025 | Lost in Variation: An Unsupervised Methodology for Mining Lexico-syntactic Patterns in Middle Arabic Texts | While MSA and some dialects of Arabic have been extensively studied in NLP, Middle Arabic is still very much unknown to the field. However, Middle Arabic holds issues that are still not covered: it is characterized by variation since it mixes standard features, colloquial ones, as well as features that belong to neithe... |
alahmari-2025-sadslyc | 2,025 | SADSLyC: A Corpus for Saudi Arabian Multi-dialect Identification through Song Lyrics | This paper presents the Saudi Arabian Dialects Song Lyrics Corpus (SADSLyC), the first dataset featuring song lyrics from the five major Saudi dialects: Najdi (Central Region), Hijazi (Western Region), Shamali (Northern Region), Janoubi (Southern Region), and Shargawi (Eastern Region). The dataset consists of 31,358 se... |
hossain-etal-2025-enhancing | 2,025 | Enhancing Dialectal Arabic Intent Detection through Cross-Dialect Multilingual Input Augmentation | Addressing the challenges of Arabic intent detection amid extensive dialectal variation, this study presents a crossdialtectal, multilingual approach for classifying intents in banking and migration contexts. By augmenting dialectal inputs with Modern Standard Arabic (MSA) and English translations, our method leverages... |
khered-etal-2025-dial2msa | 2,025 | Dial2MSA-Verified: A Multi-Dialect Arabic Social Media Dataset for Neural Machine Translation to Modern Standard Arabic | Social media has become an essential focus for Natural Language Processing (NLP) research due to its widespread use and unique linguistic characteristics. Normalising social media content, especially for morphologically rich languages like Arabic, remains a complex task due to limited parallel corpora. Arabic encompass... |
el-ghawi-2025-web | 2,025 | Web-Based Corpus Compilation of the Emirati Arabic Dialect | This paper displays some initial efforts conducted in the compilation pursuits of Arabic dialectal corpora in the form of raw text, the end purpose of which is to fine-tune existing Arabic large language models (LLM) to better understand and generate text in the Emirati dialect as instructed. The focus of the paper is ... |
al-laith-kebdani-2025-evaluating | 2,025 | Evaluating Calibration of Arabic Pre-trained Language Models on Dialectal Text | While pre-trained language models have made significant progress in different classification tasks, little attention has been given to the reliability of their confidence scores. Calibration, how well model confidence aligns with actual accuracy, is essential for real-world applications where decisions rely on probabil... |
aftiss-etal-2025-empirical | 2,025 | Empirical Evaluation of Pre-trained Language Models for Summarizing Moroccan Darija News Articles | Moroccan Dialect (MD), or {\textquotedblleft}Darija,{\textquotedblright} is a primary spoken variant of Arabic in Morocco, yet remains underrepresented in Natural Language Processing (NLP) research, particularly in tasks like summarization. Despite a growing volume of MD textual data online, there is a lack of robust r... |
chafik-etal-2025-dialect2sql | 2,025 | Dialect2SQL: A Novel Text-to-SQL Dataset for Arabic Dialects with a Focus on Moroccan Darija | The task of converting natural language questions into executable SQL queries, known as text-to-SQL, has gained significant interest in recent years, as it enables non-technical users to interact with relational databases. Many benchmarks, such as SPIDER and WikiSQL, have contributed to the development of new models an... |
bouomar-abbas-2025-arasim | 2,025 | AraSim: Optimizing Arabic Dialect Translation in Children`s Literature with LLMs and Similarity Scores | The goal of the paper is to address the linguistic gap faced by young Egyptian Arabic speakers through translating children stories from Modern Standard Arabic to the Egyptian Cairo dialect. Claude is used for initial translation, and a fine-tuned AraT5 model is used for backtranslation. The translation quality is asse... |
haj-ahmed-etal-2025-navigating | 2,025 | Navigating Dialectal Bias and Ethical Complexities in Levantine Arabic Hate Speech Detection | Social media platforms have become central to global communication, yet they also facilitate the spread of hate speech. For underrepresented dialects like Levantine Arabic, detecting hate speech presents unique cultural, ethical, and linguistic challenges. This paper explores the complex sociopolitical and linguistic l... |
scherrer-etal-2025-findings | 2,025 | Findings of the VarDial Evaluation Campaign 2025: The NorSID Shared Task on Norwegian Slot, Intent and Dialect Identification | The VarDial Evaluation Campaign 2025 was organized as part of the twelfth workshop on Natural Language Processing for Similar Languages, Varieties and Dialects (VarDial), colocated with COLING 2025. It consisted of one shared task with three subtasks: intent detection, slot filling and dialect identification for Norweg... |
alves-2025-information | 2,025 | Information Theory and Linguistic Variation: A Study of Brazilian and European Portuguese | We present a general analysis of the lexical and grammatical differences between Brazilian and European Portuguese by applying entropy measures, including Kullback-Leibler divergence and word order entropy, across various linguistic levels. Using a parallel corpus of BP and EP sentences translated from English, we quan... |
ng-markov-2025-leveraging | 2,025 | Leveraging Open-Source Large Language Models for Native Language Identification | Native Language Identification (NLI) {--} the task of identifying the native language (L1) of a person based on their writing in the second language (L2) {--} has applications in forensics, marketing, and second language acquisition. Historically, conventional machine learning approaches that heavily rely on extensive ... |
torgbi-etal-2025-adapting | 2,025 | Adapting Whisper for Regional Dialects: Enhancing Public Services for Vulnerable Populations in the United Kingdom | We collect novel data in the public service domain to evaluate the capability of the state-of-the-art automatic speech recognition (ASR) models in capturing regional differences in accents in the United Kingdom (UK), specifically focusing on two accents from Scotland with distinct dialects. This study addresses real-wo... |
alam-anastasopoulos-2025-large | 2,025 | Large Language Models as a Normalizer for Transliteration and Dialectal Translation | NLP models trained on standardized language data often struggle with variations. We assess various Large Language Models (LLMs) for transliteration and dialectal normalization. Tuning open-source LLMs with as little as 10,000 parallel examples using LoRA can achieve results comparable to or better than closed-source LL... |
faisal-anastasopoulos-2025-testing | 2,025 | Testing the Boundaries of LLMs: Dialectal and Language-Variety Tasks | This study evaluates the performance of large language models (LLMs) on benchmark datasets designed for dialect-specific NLP tasks. Dialectal NLP is a low-resource field, yet it is crucial for evaluating the robustness of language models against linguistic diversity. This work is the first to systematically compare sta... |
plum-etal-2025-text | 2,025 | Text Generation Models for Luxembourgish with Limited Data: A Balanced Multilingual Strategy | This paper addresses the challenges in developing language models for less-represented languages, with a focus on Luxembourgish. Despite its active development, Luxembourgish faces a digital data scarcity, exacerbated by Luxembourg`s multilingual context. We propose a novel text generation model based on the T5 archite... |
lendvai-etal-2025-retrieval | 2,025 | Retrieval of Parallelizable Texts Across Church Slavic Variants | The goal of our study is to identify parallelizable texts for Church Slavic, across chronological and regional variants. Next to using a benchmark text, we utilize a recently digitized, large text collection and compile new resources for the retrieval of similar texts: a ground truth dataset holding a small amount of m... |
lutgen-etal-2025-neural | 2,025 | Neural Text Normalization for Luxembourgish Using Real-Life Variation Data | Orthographic variation is very common in Luxembourgish texts due to the absence of a fully-fledged standard variety. Additionally, developing NLP tools for Luxembourgish is a difficult task given the lack of annotated and parallel data, which is exacerbated by ongoing standardization. In this paper, we propose the firs... |
kruckl-etal-2025-improving | 2,025 | Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case Study | Reliable slot and intent detection (SID) is crucial in natural language understanding for applications like digital assistants. Encoder-only transformer models fine-tuned on high-resource languages generally perform well on SID. However, they struggle with dialectal data, where no standardized form exists and training ... |
coats-etal-2025-regional | 2,025 | Regional Distribution of the /el/-/\ael/ Merger in Australian English | Prelateral merger of /e/ and /{\ae}/ is a salient acoustic feature of speech from Melbourne and the state of Victoria in Australia, but little is known about its presence in other parts of the country. In this study, automated methods of data collection, forced alignment, and formant extraction are used to analyze the ... |
khalifa-etal-2025-learning | 2,025 | Learning Cross-Dialectal Morphophonology with Syllable Structure Constraints | We investigate learning surface forms from underlying morphological forms for low-resource language varieties. We concentrate on learning explicit rules with the aid of learned syllable structure constraints, which outperforms neural methods on this small data task and provides interpretable output. Evaluating across o... |
lopetegui-etal-2025-common | 2,025 | Common Ground, Diverse Roots: The Difficulty of Classifying Common Examples in Spanish Varieties | Variations in languages across geographic regions or cultures are crucial to address to avoid biases in NLP systems designed for culturally sensitive tasks, such as hate speech detection or dialog with conversational agents. In languages such as Spanish, where varieties can significantly overlap, many examples can be v... |
blaschke-etal-2025-add | 2,025 | Add Noise, Tasks, or Layers? MaiNLP at the VarDial 2025 Shared Task on Norwegian Dialectal Slot and Intent Detection | Slot and intent detection (SID) is a classic natural language understanding task. Despite this, research has only more recently begun focusing on SID for dialectal and colloquial varieties. Many approaches for low-resource scenarios have not yet been applied to dialectal SID data, or compared to each other on the same ... |
midtgaard-etal-2025-ltg | 2,025 | LTG at VarDial 2025 NorSID: More and Better Training Data for Slot and Intent Detection | This paper describes the LTG submission to the VarDial 2025 shared task, where we participate in the Norwegian slot and intent detection subtasks. The shared task focuses on Norwegian dialects, which present challenges due to their low-resource nature and variation. We test a variety of neural models and training data ... |
bengoetxea-etal-2025-hitz | 2,025 | HiTZ at VarDial 2025 NorSID: Overcoming Data Scarcity with Language Transfer and Automatic Data Annotation | In this paper we present our submission for the NorSID Shared Task as part of the 2025 VarDial Workshop, consisting of three tasks: Intent Detection, Slot Filling and Dialect Identification, evaluated using data in different dialects of the Norwegian language. For Intent Detection and Slot Filling, we have fine-tuned a... |
ibrahim-2025-cufe | 2,025 | CUFE@VarDial 2025 NorSID: Multilingual BERT for Norwegian Dialect Identification and Intent Detection | Dialect identification is crucial in enhancing various tasks, including sentiment analysis, as a speaker`s geographical origin can significantly affect their perspective on a topic, also, intent detection has gained significant traction in natural language processing due to its applications in various domains, includin... |
pavlich-etal-2025-beyond | 2,025 | Beyond Text-to-SQL for IoT Defense: A Comprehensive Framework for Querying and Classifying IoT Threats | Recognizing the promise of natural language interfaces to databases, prior studies have emphasized the development of text-to-SQL systems. Existing research has generally focused on generating SQL statements from text queries, and the broader challenge lies in inferring new information about the returned data. Our rese... |
cheng-etal-2025-gibberish | 2,025 | Gibberish is All You Need for Membership Inference Detection in Contrastive Language-Audio Pretraining | Audio can disclose PII, particularly when combined with related text data. Therefore, it is essential to develop tools to detect privacy leakage in Contrastive Language-Audio Pretraining(CLAP). Existing MIAs need audio as input, risking exposure of voiceprint and requiring costly shadow models. We first propose PRMID, ... |
cheng-etal-2025-pbi | 2,025 | PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization | Understanding the vulnerabilities of Large Vision Language Models (LVLMs) to jailbreak attacks is essential for their responsible real-world deployment. Most previous work requires access to model gradients, or is based on human knowledge (prompt engineering) to complete jailbreak, and they hardly consider the interact... |
shi-etal-2025-ambiguity | 2,025 | Ambiguity Detection and Uncertainty Calibration for Question Answering with Large Language Models | Large Language Models (LLMs) have demonstrated excellent capabilities in Question Answering (QA) tasks, yet their ability to identify and address ambiguous questions remains underdeveloped. Ambiguities in user queries often lead to inaccurate or misleading answers, undermining user trust in these systems. Despite prior... |
liu-etal-2025-smaller | 2,025 | Smaller Large Language Models Can Do Moral Self-Correction | Self-correction is one of the most amazing emerging capabilities of Large Language Models (LLMs), enabling LLMs to self-modify an inappropriate output given a natural language feedback which describes the problems of that output. Moral self-correction is a post-hoc approach correcting unethical generations without requ... |
bonnier-2025-error | 2,025 | Error Detection for Multimodal Classification | Machine learning models have proven to be useful in various key applications such as autonomous driving or diagnosis prediction. When a model is implemented under real-world conditions, it is thus essential to detect potential errors with a trustworthy approach. This monitoring practice will render decision-making safe... |
kim-cho-2025-break | 2,025 | Break the Breakout: Reinventing LM Defense Against Jailbreak Attacks with Self-Refine | Language models (LMs) are vulnerable to exploitation for adversarial misuse. Training LMs for safety alignment is extensive, making it hard to respond to fast-developing attacks immediately, such as jailbreaks. We propose self-refine with formatting that achieves outstanding safety even in non-safety-aligned LMsand eva... |
li-etal-2025-minimal | 2,025 | Minimal Evidence Group Identification for Claim Verification | When verifying a claim in real-world settings, e.g. against a large collection of candidate evidence text retrieved from the web, a model is typically expected to identify and aggregate a complete set of evidence pieces that collectively provide full support to a claim.The problem becomes particularly challenging as th... |
wei-etal-2025-cracking | 2,025 | Cracking the Code: Enhancing Implicit Hate Speech Detection through Coding Classification | The internet has become a hotspot for hate speech (HS), threatening societal harmony and individual well-being. While automatic detection methods perform well in identifying explicit hate speech (ex-HS), they struggle with more subtle forms, such as implicit hate speech (im-HS). We tackle this problem by introducing a ... |
kale-vrn-2025-line | 2,025 | Line of Duty: Evaluating LLM Self-Knowledge via Consistency in Feasibility Boundaries | As LLMs grow more powerful, their most profound achievement may be recognising when to say {\textquotedblleft}I don`t know{\textquotedblright}. Existing studies on LLM self-knowledge have been largely constrained by human-defined notions of feasibility, often neglecting the reasons behind unanswerability by LLMs and fa... |
singhania-etal-2025-multi | 2,025 | Multi-lingual Multi-turn Automated Red Teaming for LLMs | Language Model Models (LLMs) have improved dramatically in the past few years, increasing their adoption and the scope of their capabilities over time. A significant amount of work is dedicated to {\textquotedblleft}model alignment{\textquotedblright}, i.e., preventing LLMs to generate unsafe responses when deployed in... |
krasnodebska-etal-2025-rainbow | 2,025 | Rainbow-Teaming for the Polish Language: A Reproducibility Study | The development of multilingual large language models (LLMs) presents challenges in evaluating their safety across all supported languages. Enhancing safety in one language (e.g., English) may inadvertently introduce vulnerabilities in others. To address this issue, we implement a methodology for the automatic creation... |
xu-etal-2025-biasedit | 2,025 | BiasEdit: Debiasing Stereotyped Language Models via Model Editing | Previous studies have established that language models manifest stereotyped biases. Existing debiasing strategies, such as retraining a model with counterfactual data, representation projection, and prompting often fail to efficiently eliminate bias or directly alter the models' biased internal representations. To addr... |
rawte-etal-2025-voters | 2,025 | Do Voters Get the Information They Want? Understanding Authentic Voter FAQs in the US and How to Improve for Informed Electoral Participation | Accurate information is crucial for democracy as it empowers voters to make informed decisions about their representatives and keeping them accountable. In the US, state election commissions (SECs), often required by law, are the primary providers of Frequently Asked Questions (FAQs) to voters, and secondary sources li... |
rawte-etal-2025-vibe | 2,025 | ViBe: A Text-to-Video Benchmark for Evaluating Hallucination in Large Multimodal Models | Recent advances in Large Multimodal Models (LMMs) have expanded their capabilities to video understanding, with Text-to-Video (T2V) models excelling in generating videos from textual prompts. However, they still frequently produce hallucinated content, revealing AI-generated inconsistencies. We introduce ViBe \url{http... |
borszukovszki-etal-2025-know | 2,025 | Know What You do Not Know: Verbalized Uncertainty Estimation Robustness on Corrupted Images in Vision-Language Models | To leverage the full potential of Large Language Models (LLMs) it is crucial to have some information on their answers' uncertainty. This means that the model has to be able to quantify how certain it is in the correctness of a given response. Bad uncertainty estimates can lead to overconfident wrong answers underminin... |
rahman-harris-2025-summary | 2,025 | Summary the Savior: Harmful Keyword and Query-based Summarization for LLM Jailbreak Defense | Large Language Models (LLMs) are widely used for their capabilities, but face threats from jailbreak attacks, which exploit LLMs to generate inappropriate information and bypass their defense system. Existing defenses are often specific to jailbreak attacks and as a result, a robust, attack-independent solution is need... |
yang-etal-2025-bias | 2,025 | Bias A-head? Analyzing Bias in Transformer-Based Language Model Attention Heads | Transformer-based pretrained large language models (PLM) such as BERT and GPT have achieved remarkable success in NLP tasks. However, PLMs are prone to encoding stereotypical biases. Although a burgeoning literature has emerged on stereotypical bias mitigation in PLMs, such as work on debiasing gender and racial stereo... |
trusca-allein-2025-mimicking | 2,025 | Mimicking How Humans Interpret Out-of-Context Sentences Through Controlled Toxicity Decoding | Interpretations of a single sentence can vary, particularly when its context is lost. This paper aims to simulate how readers perceive content with varying toxicity levels by generating diverse interpretations of out-of-context sentences. By modeling toxicity we can anticipate misunderstandings and reveal hidden toxic ... |
rabinovich-anaby-tavor-2025-robustness | 2,025 | On the Robustness of Agentic Function Calling | Large Language Models (LLMs) are increasingly acting as autonomous agents, with function calling (FC) capabilities enabling them to invoke specific tools for tasks. While prior research has primarily focused on improving FC accuracy, little attention has been given to the robustness of these agents to perturbations in ... |
cecere-etal-2025-monte | 2,025 | Monte Carlo Temperature: a robust sampling strategy for LLM`s uncertainty quantification methods | Uncertainty quantification (UQ) in Large Language Models (LLMs) is essential for their safe and reliable deployment, particularly in critical applications where incorrect outputs can have serious consequences. Current UQ methods typically rely on querying the model multiple times using non-zero temperature sampling to ... |
bhat-etal-2025-know | 2,025 | Know Thyself: Validating Knowledge Awareness of LLM-based Persona Agents | Large Language Models (LLMs) have demonstrated remarkable capability in simulating human behaviors, personality, and language. Such synthetic agents with personalities are considered as cost-effective proxies for real users to facilitate crowd-sourcing efforts like annotations, surveys, and A/B testing. Accordingly, it... |
purpura-etal-2025-building | 2,025 | Building Safe GenAI Applications: An End-to-End Overview of Red Teaming for Large Language Models | The rapid growth of Large Language Models (LLMs) presents significant privacy, security, and ethical concerns. While much research has proposed methods for defending LLM systems against misuse by malicious actors, researchers have recently complemented these efforts with an offensive approach that involves red teaming,... |
angelov-etal-2025-difficulty | 2,025 | Difficulty Estimation in Natural Language Tasks with Action Scores | This study investigates the effectiveness of the action score, a metric originally developed for computer vision tasks, in estimating sample difficulty across various natural language processing (NLP) tasks. Using transformer-based models, the action score is applied to sentiment analysis, natural language inference, a... |
sinha-etal-2025-small | 2,025 | Are Small Language Models Ready to Compete with Large Language Models for Practical Applications? | The rapid rise of Language Models (LMs) has expanded their use in several applications. Yet, due to constraints of model size, associated cost, or proprietary restrictions, utilizing state-of-the-art (SOTA) LLMs is not always feasible. With open, smaller LMs emerging, more applications can leverage their capabilities, ... |
bodhwani-etal-2025-calibrated | 2,025 | A Calibrated Reflection Approach for Enhancing Confidence Estimation in LLMs | A critical challenge in deploying Large Language Models (LLMs) is developing reliable mechanisms to estimate their confidence, enabling systems to determine when to trust model outputs and when to seek human intervention. In this paper, we present a Calibrated Reflection Approach for Enhancing Confidence Estimation in ... |
cao-wang-2025-evaluating | 2,025 | Evaluating Design Choices in Verifiable Generation with Open-source Models | Verifiable generation is introduced to improve the transparency and trustworthiness of outputs produced by large language models (LLMs). Recent studies observe that open-source models struggle to include accurate citations to supporting documents in their generation with in-context learning, in contrast to the strong p... |
sakib-etal-2025-battling | 2,025 | Battling Misinformation: An Empirical Study on Adversarial Factuality in Open-Source Large Language Models | Adversarial factuality refers to the deliberate insertion of misinformation into input prompts by an adversary, characterized by varying levels of expressed confidence. In this study, we systematically evaluate the performance of several open-source large language models (LLMs) when exposed to such adversarial inputs. ... |
chance-etal-2025-will | 2,025 | Will the Prince Get True Love`s Kiss? On the Model Sensitivity to Gender Perturbation over Fairytale Texts | In this paper, we study whether language models are affected by learned gender stereotypes during the comprehension of stories. Specifically, we investigate how models respond to gender stereotype perturbations through counterfactual data augmentation. Focusing on Question Answering (QA) tasks in fairytales, we modify ... |
karwa-singh-2025-disentangling | 2,025 | Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings | Understanding the inner workings of neural embeddings, particularly in models such as BERT, remains a challenge because of their high-dimensional and opaque nature. This paper proposes a framework for uncovering the specific dimensions of vector embeddings that encode distinct linguistic properties (LPs). We introduce ... |
zakizadeh-pilehvar-2025-gender | 2,025 | Gender Encoding Patterns in Pretrained Language Model Representations | Gender bias in pretrained language models (PLMs) poses significant social and ethical challenges. Despite growing awareness, there is a lack of comprehensive investigation into how different models internally represent and propagate such biases. This study adopts an information-theoretic approach to analyze how gender ... |
rawte-etal-2025-defining | 2,025 | Defining and Quantifying Visual Hallucinations in Vision-Language Models | The troubling rise of hallucination presents perhaps the most significant impediment to the advancement of responsible AI. In recent times, considerable research has focused on detecting and mitigating hallucination in Large Language Models (LLMs). However, it`s worth noting that hallucination is also quite prevalent i... |
etzine-etal-2025-revitalizing | 2,025 | Revitalizing Saturated Benchmarks: A Weighted Metric Approach for Differentiating Large Language Model Performance | Existing benchmarks are becoming saturated and less effective in evaluating model performance due to factors such as data contamination and the advancing capabilities of the Large Language Models (LLMs). This paper introduces EMDM (Enhanced Model Differentiation Metric), a novel weighted metric designed to revitalize e... |
labrak-etal-2025-synthetic | 2,025 | Synthetic Lyrics Detection Across Languages and Genres | In recent years, the use of large language models (LLMs) to generate music content, particularly lyrics, has gained in popularity. These advances provide valuable tools for artists and enhance their creative processes, but they also raise concerns about copyright violations, consumer satisfaction, and content spamming.... |
zhang-etal-2025-lightweight | 2,025 | A Lightweight Multi Aspect Controlled Text Generation Solution For Large Language Models | Multi-Aspect Controllable Text Generation (MCTG) introduces fine-grained multiple constraints in natural language generation, i.e. control attributes in topics, sentiments, and detoxification.MCTG demonstrates application prospects for trustworthy generation of Large Language Models (LLMs) but is limited by generalizat... |
ding-etal-2025-gender | 2,025 | Gender Bias in Large Language Models across Multiple Languages: A Case Study of ChatGPT | With the growing deployment of large language models (LLMs) across various applications, assessing the influence of gender biases embedded in LLMs becomes crucial. The topic of gender bias within the realm of natural language processing (NLP) has gained considerable focus, particularly in the context of English. Noneth... |
varshney-etal-2025-investigating | 2,025 | Investigating and Addressing Hallucinations of LLMs in Tasks Involving Negation | Large Language Models (LLMs) have achieved remarkable performance across a wide variety of natural language tasks. However, they have been shown to suffer from a critical limitation pertinent to {\textquoteleft}hallucination' in their output. Recent research has focused on investigating and addressing this problem for ... |
zevallos-etal-2025-first | 2,025 | The First Multilingual Model For The Detection of Suicide Texts | Suicidal ideation is a serious health problem affecting millions of people worldwide. Social networks provide information about these mental health problems through users' emotional expressions. We propose a multilingual model leveraging transformer architectures like mBERT, XML-R, and mT5 to detect suicidal text acros... |
lin-etal-2025-crossin | 2,025 | CrossIn: An Efficient Instruction Tuning Approach for Cross-Lingual Knowledge Alignment | Multilingual proficiency presents a significant challenge for large language models (LLMs). English-centric models are usually suboptimal in other languages, particularly those that are linguistically distant from English. This performance discrepancy mainly stems from the imbalanced distribution of training data acros... |
srirag-etal-2025-evaluating | 2,025 | Evaluating Dialect Robustness of Language Models via Conversation Understanding | With an evergrowing number of LLMs reporting superlative performance for English, their ability to perform equitably for different dialects of English (i.e., dialect robustness) needs to be ascertained. Specifically, we use English language (US English or Indian English) conversations between humans who play the word-g... |
tashu-etal-2025-cross | 2,025 | Cross-Lingual Document Recommendations with Transformer-Based Representations: Evaluating Multilingual Models and Mapping Techniques | Recommendation systems, for documents, have become tools for finding relevant content on the Web. However, these systems have limitations when it comes to recommending documents in languages different from the query language, which means they might overlook resources in non-native languages. This research focuses on re... |
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ACL abstracts extracted from the official ACL Anthology BibTeX.
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