NAACL
Collection
Accepted papers for NAACL (Annual Conference of the North American Chapter of the Association for Computational Linguistics), one dataset per year. • 9 items • Updated
paper_id stringlengths 17 19 | title stringlengths 22 160 | paper_url stringlengths 43 45 | authors listlengths 1 54 | abstract large_stringlengths 531 1.95k ⌀ | anthology_id stringlengths 17 19 | doi stringlengths 29 31 | award stringclasses 0
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|
2025.naacl-long.1 | Understanding Figurative Meaning through Explainable Visual Entailment | https://aclanthology.org/2025.naacl-long.1/ | [
"Arkadiy Saakyan",
"Shreyas Kulkarni",
"Tuhin Chakrabarty",
"Smaranda Muresan"
] | Large Vision-Language Models (VLMs) have demonstrated strong capabilities in tasks requiring a fine-grained understanding of literal meaning in images and text, such as visual question-answering or visual entailment. However, there has been little exploration of the capabilities of these models when presented with imag... | 2025.naacl-long.1 | 10.18653/v1/2025.naacl-long.1 | null | 2405.01474 | title_snapshot |
2025.naacl-long.2 | Benchmarking Distributional Alignment of Large Language Models | https://aclanthology.org/2025.naacl-long.2/ | [
"Nicole Meister",
"Carlos Guestrin",
"Tatsunori Hashimoto"
] | Language models (LMs) are increasingly used as simulacra for people, yet their ability to match the distribution of views of a specific demographic group and be distributionally aligned remains uncertain. This notion of distributional alignment is complex, as there is significant variation in the types of attributes th... | 2025.naacl-long.2 | 10.18653/v1/2025.naacl-long.2 | null | 2411.05403 | title_snapshot |
2025.naacl-long.3 | World Models with Hints of Large Language Models for Goal Achieving | https://aclanthology.org/2025.naacl-long.3/ | [
"Zeyuan Liu",
"Ziyu Huan",
"Xiyao Wang",
"Jiafei Lyu",
"Jian Tao",
"Xiu Li",
"Furong Huang",
"Huazhe Xu"
] | Reinforcement learning struggles in the face of long-horizon tasks and sparse goals due to the difficulty in manual reward specification. While existing methods address this by adding intrinsic rewards, they may fail to provide meaningful guidance in long-horizon decision-making tasks with large state and action spaces... | 2025.naacl-long.3 | 10.18653/v1/2025.naacl-long.3 | null | 2406.07381 | title_snapshot |
2025.naacl-long.4 | CogLM: Tracking Cognitive Development of Large Language Models | https://aclanthology.org/2025.naacl-long.4/ | [
"Xinglin Wang",
"Peiwen Yuan",
"Shaoxiong Feng",
"Yiwei Li",
"Boyuan Pan",
"Heda Wang",
"Yao Hu",
"Kan Li"
] | Piaget’s Theory of Cognitive Development (PTC) posits that the development of cognitive levels forms the foundation for human learning across various abilities. As Large Language Models (LLMs) have recently shown remarkable abilities across a wide variety of tasks, we are curious about the cognitive levels of current L... | 2025.naacl-long.4 | 10.18653/v1/2025.naacl-long.4 | null | 2408.09150 | title_snapshot |
2025.naacl-long.5 | Improving and Assessing the Fidelity of Large Language Models Alignment to Online Communities | https://aclanthology.org/2025.naacl-long.5/ | [
"Minh Duc Chu",
"Zihao He",
"Rebecca Dorn",
"Kristina Lerman"
] | Large language models (LLMs) have shown promise in representing individuals and communities, offering new ways to study complex social dynamics. However, effectively aligning LLMs with specific human groups and systematically assessing the fidelity of the alignment remains a challenge. This paper presents a robust fram... | 2025.naacl-long.5 | 10.18653/v1/2025.naacl-long.5 | null | 2408.09366 | title_snapshot |
2025.naacl-long.6 | Improving Retrospective Language Agents via Joint Policy Gradient Optimization | https://aclanthology.org/2025.naacl-long.6/ | [
"Xueyang Feng",
"Bo Lan",
"Quanyu Dai",
"Lei Wang",
"Jiakai Tang",
"Xu Chen",
"Zhenhua Dong",
"Ji-Rong Wen"
] | In recent research advancements within the community, large language models (LLMs) have sparked great interest in creating autonomous agents. However, current prompt-based agents often heavily rely on large-scale LLMs. Meanwhile, although fine-tuning methods significantly enhance the capabilities of smaller LLMs, the f... | 2025.naacl-long.6 | 10.18653/v1/2025.naacl-long.6 | null | 2503.01490 | title_snapshot |
2025.naacl-long.7 | CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases | https://aclanthology.org/2025.naacl-long.7/ | [
"Xiangyan Liu",
"Bo Lan",
"Zhiyuan Hu",
"Yang Liu",
"Zhicheng Zhang",
"Fei Wang",
"Michael Qizhe Shieh",
"Wenmeng Zhou"
] | Large Language Models (LLMs) excel in stand-alone code tasks like HumanEval and MBPP, but struggle with handling entire code repositories. This challenge has prompted research on enhancing LLM-codebase interaction at a repository scale. Current solutions rely on similarity-based retrieval or manual tools and APIs, each... | 2025.naacl-long.7 | 10.18653/v1/2025.naacl-long.7 | null | 2408.03910 | title_snapshot |
2025.naacl-long.8 | Instantly Learning Preference Alignment via In-context DPO | https://aclanthology.org/2025.naacl-long.8/ | [
"Feifan Song",
"Yuxuan Fan",
"Xin Zhang",
"Peiyi Wang",
"Houfeng Wang"
] | Human Preference Alignment (HPA) can assist large language models (LLMs) to generate safe content. Due to the heavy cost of fine-tuning, tuning-free methods have emerged, typically modifying LLM decoding via post-processing. In this paper, we propose a novel and effective approach for HPA in a tuning-free way, named In... | 2025.naacl-long.8 | 10.18653/v1/2025.naacl-long.8 | null | null | null |
2025.naacl-long.9 | ALTER: Augmentation for Large-Table-Based Reasoning | https://aclanthology.org/2025.naacl-long.9/ | [
"Han Zhang",
"Yuheng Ma",
"Hanfang Yang"
] | null | 2025.naacl-long.9 | 10.18653/v1/2025.naacl-long.9 | null | 2407.03061 | title_snapshot |
2025.naacl-long.10 | What the #?*!: Disentangling Hate Across Target Identities | https://aclanthology.org/2025.naacl-long.10/ | [
"Yiping Jin",
"Leo Wanner",
"Aneesh Moideen Koya"
] | Hate speech (HS) classifiers do not perform equally well in detecting hateful expressions towards different target identities. They also demonstrate systematic biases in predicted hatefulness scores. Tapping on two recently proposed functionality test datasets for HS detection, we quantitatively analyze the impact of d... | 2025.naacl-long.10 | 10.18653/v1/2025.naacl-long.10 | null | 2410.10332 | title_judge |
2025.naacl-long.11 | MAD Speech: Measures of Acoustic Diversity of Speech | https://aclanthology.org/2025.naacl-long.11/ | [
"Matthieu Futeral",
"Andrea Agostinelli",
"Marco Tagliasacchi",
"Neil Zeghidour",
"Eugene Kharitonov"
] | Generative spoken language models produce speech in a wide range of voices, prosody, and recording conditions, seemingly approaching the diversity of natural speech. However, the extent to which generated speech is acoustically diverse remains unclear due to a lack of appropriate metrics. We address this gap by develop... | 2025.naacl-long.11 | 10.18653/v1/2025.naacl-long.11 | null | 2404.10419 | title_snapshot |
2025.naacl-long.12 | The Russian-focused embedders’ exploration: ruMTEB benchmark and Russian embedding model design | https://aclanthology.org/2025.naacl-long.12/ | [
"Artem Snegirev",
"Maria Tikhonova",
"Maksimova Anna",
"Alena Fenogenova",
"Aleksandr Abramov"
] | Embedding models play a crucial role in Natural Language Processing (NLP) by creating text embeddings used in various tasks such as information retrieval and assessing semantic text similarity. This paper focuses on research related to embedding models in the Russian language. It introduces a new Russian-focused embedd... | 2025.naacl-long.12 | 10.18653/v1/2025.naacl-long.12 | null | 2408.12503 | title_snapshot |
2025.naacl-long.13 | PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries | https://aclanthology.org/2025.naacl-long.13/ | [
"Mingwen Dong",
"Nischal Ashok Kumar",
"Yiqun Hu",
"Anuj Chauhan",
"Chung-Wei Hang",
"Shuaichen Chang",
"Lin Pan",
"Wuwei Lan",
"Henghui Zhu",
"Jiarong Jiang",
"Patrick Ng",
"Zhiguo Wang"
] | Previous text-to-SQL datasets and systems have primarily focused on user questions with clear intentions that can be answered. However, real user questions can often be ambiguous with multiple interpretations or unanswerable due to a lack of relevant data. In this work, we construct a practical conversational text-to-S... | 2025.naacl-long.13 | 10.18653/v1/2025.naacl-long.13 | null | 2410.11076 | title_snapshot |
2025.naacl-long.14 | MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems | https://aclanthology.org/2025.naacl-long.14/ | [
"Nandan Thakur",
"Suleman Kazi",
"Ge Luo",
"Jimmy Lin",
"Amin Ahmad"
] | Traditional retrieval-augmented generation (RAG) benchmarks evaluate systems using heuristic-based metrics, but these require human preferences as the ground truth for reference. In contrast, arena-based benchmarks, where systems compete against each other, require an expensive large language model (LLM) as a judge for... | 2025.naacl-long.14 | 10.18653/v1/2025.naacl-long.14 | null | 2410.13716 | title_snapshot |
2025.naacl-long.15 | LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs | https://aclanthology.org/2025.naacl-long.15/ | [
"Do Xuan Long",
"Hai Nguyen Ngoc",
"Tiviatis Sim",
"Hieu Dao",
"Shafiq Joty",
"Kenji Kawaguchi",
"Nancy F. Chen",
"Min-Yen Kan"
] | We present the first systematic evaluation examining format bias in performance of large language models (LLMs). Our approach distinguishes between two categories of an evaluation metric under format constraints to reliably and accurately assess performance: one measures performance when format constraints are adhered ... | 2025.naacl-long.15 | 10.18653/v1/2025.naacl-long.15 | null | 2408.08656 | title_snapshot |
2025.naacl-long.16 | The Impact of Visual Information in Chinese Characters: Evaluating Large Models’ Ability to Recognize and Utilize Radicals | https://aclanthology.org/2025.naacl-long.16/ | [
"Xiaofeng Wu",
"Karl Stratos",
"Wei Xu"
] | The glyphic writing system of Chinese incorporates information-rich visual features in each character, such as radicals that provide hints about meaning or pronunciation. However, there has been no investigation into whether contemporary Large Language Models (LLMs) and Vision-Language Models (VLMs) can harness these s... | 2025.naacl-long.16 | 10.18653/v1/2025.naacl-long.16 | null | 2410.09013 | title_snapshot |
2025.naacl-long.17 | PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from related Example Banks | https://aclanthology.org/2025.naacl-long.17/ | [
"Soumya Suvra Ghosal",
"Soumyabrata Pal",
"Koyel Mukherjee",
"Dinesh Manocha"
] | Large Language Models (LLMs) have recently demonstrated impressive few-shot learning capabilities through in-context learning (ICL). However, ICL performance is highly dependent on the choice of few-shot demonstrations, making the selection of the most optimal examples a persistent research challenge. This issue is fur... | 2025.naacl-long.17 | 10.18653/v1/2025.naacl-long.17 | null | 2412.05710 | title_snapshot |
2025.naacl-long.18 | Unlocking Decoding-time Controllability: Gradient-Free Multi-Objective Alignment with Contrastive Prompts | https://aclanthology.org/2025.naacl-long.18/ | [
"Tingchen Fu",
"Yupeng Hou",
"Julian McAuley",
"Rui Yan"
] | The task of multi-objective alignment aims at balancing and controlling the different alignment objectives, e.g., helpfulness, harmlessness and honesty) of large language models to meet the personalized requirements of different users. However, previous methods tend to train multiple models to deal with various user pr... | 2025.naacl-long.18 | 10.18653/v1/2025.naacl-long.18 | null | 2408.05094 | title_snapshot |
2025.naacl-long.19 | Fingerspelling within Sign Language Translation | https://aclanthology.org/2025.naacl-long.19/ | [
"Garrett Tanzer"
] | Fingerspelling poses challenges for sign language processing due to its high-frequency motion and use for open-vocabulary terms. While prior work has studied fingerspelling recognition, there has been little attention to evaluating how well sign language translation models understand fingerspelling in the context of en... | 2025.naacl-long.19 | 10.18653/v1/2025.naacl-long.19 | null | 2408.07065 | title_snapshot |
2025.naacl-long.20 | MoDS: Moderating a Mixture of Document Speakers to Summarize Debatable Queries in Document Collections | https://aclanthology.org/2025.naacl-long.20/ | [
"Nishant Balepur",
"Alexa Siu",
"Nedim Lipka",
"Franck Dernoncourt",
"Tong Sun",
"Jordan Boyd-Graber",
"Puneet Mathur"
] | Query-focused summarization (QFS) gives a summary of documents to answer a query.Past QFS work assumes queries have one answer, ignoring debatable ones (*Is law school worth it?*).We introduce **Debatable QFS (DQFS)**, a task to create summaries that answer debatable queries via documents with opposing perspectives; su... | 2025.naacl-long.20 | 10.18653/v1/2025.naacl-long.20 | null | 2502.00322 | title_snapshot |
2025.naacl-long.21 | Aligning Sentence Simplification with ESL Learner’s Proficiency for Language Acquisition | https://aclanthology.org/2025.naacl-long.21/ | [
"Guanlin Li",
"Yuki Arase",
"Noel Crespi"
] | Text simplification is crucial for improving accessibility and comprehension for English as a Second Language (ESL) learners. This study goes a step further and aims to facilitate ESL learners’ language acquisition by simplification. Specifically, we propose simplifying complex sentences to appropriate levels for learn... | 2025.naacl-long.21 | 10.18653/v1/2025.naacl-long.21 | null | 2502.11457 | title_snapshot |
2025.naacl-long.22 | PeerQA: A Scientific Question Answering Dataset from Peer Reviews | https://aclanthology.org/2025.naacl-long.22/ | [
"Tim Baumgärtner",
"Ted Briscoe",
"Iryna Gurevych"
] | We present PeerQA, a real-world, scientific, document-level Question Answering (QA) dataset. PeerQA questions have been sourced from peer reviews, which contain questions that reviewers raised while thoroughly examining the scientific article. Answers have been annotated by the original authors of each paper. The datas... | 2025.naacl-long.22 | 10.18653/v1/2025.naacl-long.22 | null | 2502.13668 | title_snapshot |
2025.naacl-long.23 | ALiiCE: Evaluating Positional Fine-grained Citation Generation | https://aclanthology.org/2025.naacl-long.23/ | [
"Yilong Xu",
"Jinhua Gao",
"Xiaoming Yu",
"Baolong Bi",
"Huawei Shen",
"Xueqi Cheng"
] | Large Language Model (LLM) can enhance its credibility and verifiability by generating text with citations. However, existing research on citation generation is predominantly limited to sentence-level statements, neglecting the significance of positional fine-grained citations that can appear anywhere within sentences.... | 2025.naacl-long.23 | 10.18653/v1/2025.naacl-long.23 | null | 2406.13375 | title_snapshot |
2025.naacl-long.24 | An LLM-Based Approach for Insight Generation in Data Analysis | https://aclanthology.org/2025.naacl-long.24/ | [
"Alberto Sánchez Pérez",
"Alaa Boukhary",
"Paolo Papotti",
"Luis Castejón Lozano",
"Adam Elwood"
] | Generating insightful and actionable information from databases is critical in data analysis. This paper introduces a novel approach using Large Language Models (LLMs) to automatically generate textual insights. Given a multi-table database as input, our method leverages LLMs to produce concise, text-based insights tha... | 2025.naacl-long.24 | 10.18653/v1/2025.naacl-long.24 | null | 2503.11664 | title_snapshot |
2025.naacl-long.25 | WebQuality: A Large-scale Multi-modal Web Page Quality Assessment Dataset with Multiple Scoring Dimensions | https://aclanthology.org/2025.naacl-long.25/ | [
"Tao Zhang",
"Yige Wang",
"Hangyu Zhu",
"Xin Li",
"Xiang Chen",
"Tianhua Zhou",
"Jin Ma"
] | The assessment of web page quality plays a critical role in a range of downstream applications, yet there is a notable absence of datasets for the evaluation of web page quality. This research presents the pioneering task of web page quality assessment and introduces the first comprehensive, multi-modal Chinese dataset... | 2025.naacl-long.25 | 10.18653/v1/2025.naacl-long.25 | null | null | null |
2025.naacl-long.26 | UFO: A UI-Focused Agent for Windows OS Interaction | https://aclanthology.org/2025.naacl-long.26/ | [
"Chaoyun Zhang",
"Liqun Li",
"Shilin He",
"Xu Zhang",
"Bo Qiao",
"Si Qin",
"Minghua Ma",
"Yu Kang",
"Qingwei Lin",
"Saravan Rajmohan",
"Dongmei Zhang",
"Qi Zhang"
] | We introduce UFO, a UI-Fcused agent designed to fulfill user requests tailored to Windows OS applications by observing and analyzing the GUI and control information of these applications. UFO utilizes a hierarchical dual-agent framework that decomposes user requests using a divide-and-conquer approach, enabling seamles... | 2025.naacl-long.26 | 10.18653/v1/2025.naacl-long.26 | null | 2402.07939 | title_snapshot |
2025.naacl-long.27 | Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness | https://aclanthology.org/2025.naacl-long.27/ | [
"Yoo Yeon Sung",
"Maharshi Gor",
"Eve Fleisig",
"Ishani Mondal",
"Jordan Boyd-Graber"
] | Adversarial datasets should validate AI robustness by providing samples on which humans perform well, but models do not. However, as models evolve, datasets can become obsolete. Measuring whether a dataset remains adversarial is hindered by the lack of a standardized metric for measuring adversarialness. We propose ADV... | 2025.naacl-long.27 | 10.18653/v1/2025.naacl-long.27 | null | 2406.16342 | title_snapshot |
2025.naacl-long.28 | Fact-Aware Multimodal Retrieval Augmentation for Accurate Medical Radiology Report Generation | https://aclanthology.org/2025.naacl-long.28/ | [
"Liwen Sun",
"James Jialun Zhao",
"Wenjing Han",
"Chenyan Xiong"
] | Multimodal foundation models hold significant potential for automating radiology report generation, thereby assisting clinicians in diagnosing cardiac diseases. However, generated reports often suffer from serious factual inaccuracy. In this paper, we introduce a fact-aware multimodal retrieval-augmented pipeline in ge... | 2025.naacl-long.28 | 10.18653/v1/2025.naacl-long.28 | null | 2407.15268 | title_snapshot |
2025.naacl-long.29 | On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs | https://aclanthology.org/2025.naacl-long.29/ | [
"Nitay Calderon",
"Roi Reichart"
] | Recent advancements in NLP systems, particularly with the introduction of LLMs, have led to widespread adoption of these systems by a broad spectrum of users across various domains, impacting decision-making, the job market, society, and scientific research. This surge in usage has led to an explosion in NLP model inte... | 2025.naacl-long.29 | 10.18653/v1/2025.naacl-long.29 | null | 2407.19200 | title_snapshot |
2025.naacl-long.30 | Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward | https://aclanthology.org/2025.naacl-long.30/ | [
"Ruohong Zhang",
"Liangke Gui",
"Zhiqing Sun",
"Yihao Feng",
"Keyang Xu",
"Yuanhan Zhang",
"Di Fu",
"Chunyuan Li",
"Alexander G Hauptmann",
"Yonatan Bisk",
"Yiming Yang"
] | Preference modeling techniques, such as direct preference optimization (DPO), has shown effective in enhancing the generalization abilities of large language model (LLM). However, in tasks involving video instruction-following, providing informative feedback, especially for open-ended conversations, remains a significa... | 2025.naacl-long.30 | 10.18653/v1/2025.naacl-long.30 | null | 2404.01258 | title_snapshot |
2025.naacl-long.31 | FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing | https://aclanthology.org/2025.naacl-long.31/ | [
"James Seale Smith",
"Chi-Heng Lin",
"Shikhar Tuli",
"Haris Jeelani",
"Shangqian Gao",
"Yilin Shen",
"Hongxia Jin",
"Yen-Chang Hsu"
] | The rapid proliferation of large language models (LLMs) in natural language processing (NLP) has created a critical need for techniques that enable efficient deployment on memory-constrained devices without compromising performance. We present a method to prune LLMs that selectively prunes model blocks based on an impo... | 2025.naacl-long.31 | 10.18653/v1/2025.naacl-long.31 | null | 2501.14713 | title_snapshot |
2025.naacl-long.32 | Conformalized Answer Set Prediction for Knowledge Graph Embedding | https://aclanthology.org/2025.naacl-long.32/ | [
"Yuqicheng Zhu",
"Nico Potyka",
"Jiarong Pan",
"Bo Xiong",
"Yunjie He",
"Evgeny Kharlamov",
"Steffen Staab"
] | Knowledge graph embeddings (KGE) apply machine learning methods on knowledge graphs (KGs) to provide non-classical reasoning capabilities based on similarities and analogies. The learned KG embeddings are typically used to answer queries by ranking all potential answers, but rankings often lack a meaningful probabilist... | 2025.naacl-long.32 | 10.18653/v1/2025.naacl-long.32 | null | 2408.08248 | title_snapshot |
2025.naacl-long.33 | Parameter-free and Accessible Prompt Learning to Enhance Adversarial Robustness for Pre-trained Vision-Language Models | https://aclanthology.org/2025.naacl-long.33/ | [
"Xingran Zhou",
"Kun Yang",
"Changtao Miao",
"Bingyu Hu",
"Zhuoer Xu",
"Shiwen Cui",
"Changhua Meng",
"Dan Hong"
] | Large pre-trained Vision-Language Models (VLMs) have revolutionized both computer vision and natural language processing. Despite their success, adversarial examples can still mislead VLMs into producing incorrect results. This work focuses on boosting the adversarial robustness of VLMs by searching for text prompts at... | 2025.naacl-long.33 | 10.18653/v1/2025.naacl-long.33 | null | null | null |
2025.naacl-long.34 | Fine-grained Fallacy Detection with Human Label Variation | https://aclanthology.org/2025.naacl-long.34/ | [
"Alan Ramponi",
"Agnese Daffara",
"Sara Tonelli"
] | We introduce FAINA, the first dataset for fallacy detection that embraces multiple plausible answers and natural disagreement. FAINA includes over 11K span-level annotations with overlaps across 20 fallacy types on social media posts in Italian about migration, climate change, and public health given by two expert anno... | 2025.naacl-long.34 | 10.18653/v1/2025.naacl-long.34 | null | 2502.13853 | title_snapshot |
2025.naacl-long.35 | Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models | https://aclanthology.org/2025.naacl-long.35/ | [
"Hila Gonen",
"Terra Blevins",
"Alisa Liu",
"Luke Zettlemoyer",
"Noah A. Smith"
] | Despite their wide adoption, the biases and unintended behaviors of language models remain poorly understood. In this paper, we identify and characterize a phenomenon never discussed before, which we call semantic leakage, where models leak irrelevant information from the prompt into the generation in unexpected ways. ... | 2025.naacl-long.35 | 10.18653/v1/2025.naacl-long.35 | null | 2408.06518 | title_snapshot |
2025.naacl-long.36 | SELFGOAL: Your Language Agents Already Know How to Achieve High-level Goals | https://aclanthology.org/2025.naacl-long.36/ | [
"Ruihan Yang",
"Jiangjie Chen",
"Yikai Zhang",
"Siyu Yuan",
"Aili Chen",
"Kyle Richardson",
"Yanghua Xiao",
"Deqing Yang"
] | Language agents powered by large language models (LLMs) are increasingly valuable as decision-making tools in domains such as gaming and programming. However, these agents often face challenges in achieving high-level goals without detailed instructions and in adapting to environments where feedback is delayed. In this... | 2025.naacl-long.36 | 10.18653/v1/2025.naacl-long.36 | null | 2406.04784 | title_snapshot |
2025.naacl-long.37 | Familiarity: Better Evaluation of Zero-Shot Named Entity Recognition by Quantifying Label Shifts in Synthetic Training Data | https://aclanthology.org/2025.naacl-long.37/ | [
"Jonas Golde",
"Patrick Haller",
"Max Ploner",
"Fabio Barth",
"Nicolaas Jedema",
"Alan Akbik"
] | Zero-shot named entity recognition (NER) is the task of detecting named entities of specific types (such as Person or Medicine) without any training examples. Current research increasingly relies on large synthetic datasets, automatically generated to cover tens of thousands of distinct entity types, to train zero-shot... | 2025.naacl-long.37 | 10.18653/v1/2025.naacl-long.37 | null | 2412.10121 | title_snapshot |
2025.naacl-long.38 | Learning to Summarize from LLM-generated Feedback | https://aclanthology.org/2025.naacl-long.38/ | [
"Hwanjun Song",
"Taewon Yun",
"Yuho Lee",
"Jihwan Oh",
"Gihun Lee",
"Jason Cai",
"Hang Su"
] | Developing effective text summarizers remains a challenge due to issues like hallucinations, key information omissions, and verbosity in LLM-generated summaries. This work explores using LLM-generated feedback to improve summary quality by aligning the summaries with human preferences for faithfulness, completeness, an... | 2025.naacl-long.38 | 10.18653/v1/2025.naacl-long.38 | null | 2410.13116 | title_snapshot |
2025.naacl-long.39 | Hybrid Graphs for Table-and-Text based Question Answering using LLMs | https://aclanthology.org/2025.naacl-long.39/ | [
"Ankush Agarwal",
"Chaitanya Devaguptapu",
"Ganesh S"
] | Answering questions that require reasoning and aggregation across both structured (tables) and unstructured (raw text) data sources presents significant challenges. Current methods rely on fine-tuning and high-quality, human-curated data, which is difficult to obtain. Recent advances in Large Language Models (LLMs) hav... | 2025.naacl-long.39 | 10.18653/v1/2025.naacl-long.39 | null | 2501.17767 | title_snapshot |
2025.naacl-long.40 | CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models | https://aclanthology.org/2025.naacl-long.40/ | [
"Ying Nie",
"Binwei Yan",
"Tianyu Guo",
"Hao Liu",
"Haoyu Wang",
"Wei He",
"Binfan Zheng",
"Weihao Wang",
"Qiang Li",
"Weijian Sun",
"Yunhe Wang",
"Dacheng Tao"
] | Large language models (LLMs) have achieved remarkable performance on various NLP tasks, yet their potential in more challenging task like finance, has not been fully explored. In this paper, we present CFinBench: a meticulously crafted, the most comprehensive evaluation benchmark to date, for assessing the financial kn... | 2025.naacl-long.40 | 10.18653/v1/2025.naacl-long.40 | null | 2407.02301 | title_snapshot |
2025.naacl-long.41 | LLM-Based Explicit Models of Opponents for Multi-Agent Games | https://aclanthology.org/2025.naacl-long.41/ | [
"XiaoPeng Yu",
"Wanpeng Zhang",
"Zongqing Lu"
] | In multi-agent scenarios, the ability to anticipate and respond to opponents is essential, particularly in environments involving adversarial and collaborative interactions. In this paper, we introduce Explicit Models of Opponents (EMO) based on Large Language Models (LLMs), enabling agents to better predict and adapt ... | 2025.naacl-long.41 | 10.18653/v1/2025.naacl-long.41 | null | null | null |
2025.naacl-long.42 | SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters | https://aclanthology.org/2025.naacl-long.42/ | [
"Yan Yang",
"Zeguan Xiao",
"Xin Lu",
"Hongru Wang",
"Xuetao Wei",
"Hailiang Huang",
"Guanhua Chen",
"Yun Chen"
] | The widespread applications of large language models (LLMs) have brought about concerns regarding their potential misuse. Although aligned with human preference data before release, LLMs remain vulnerable to various malicious attacks. In this paper, we adopt a red-teaming strategy to enhance LLM safety and introduce Se... | 2025.naacl-long.42 | 10.18653/v1/2025.naacl-long.42 | null | 2407.01902 | title_snapshot |
2025.naacl-long.43 | JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation | https://aclanthology.org/2025.naacl-long.43/ | [
"Shota Onohara",
"Atsuyuki Miyai",
"Yuki Imajuku",
"Kazuki Egashira",
"Jeonghun Baek",
"Xiang Yue",
"Graham Neubig",
"Kiyoharu Aizawa"
] | null | 2025.naacl-long.43 | 10.18653/v1/2025.naacl-long.43 | null | 2410.17250 | title_snapshot |
2025.naacl-long.44 | EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction | https://aclanthology.org/2025.naacl-long.44/ | [
"Siyu Yuan",
"Kaitao Song",
"Jiangjie Chen",
"Xu Tan",
"Yongliang Shen",
"Kan Ren",
"Dongsheng Li",
"Deqing Yang"
] | There has been a rising interest in utilizing tools in applications of autonomous agents based on large language models (LLMs) to address intricate real-world tasks. To develop LLMbased agents, it usually requires LLMs to understand many tool functions from different tool documentations. However, these documentations c... | 2025.naacl-long.44 | 10.18653/v1/2025.naacl-long.44 | null | 2401.06201 | title_snapshot |
2025.naacl-long.45 | Decoding Hate: Exploring Language Models’ Reactions to Hate Speech | https://aclanthology.org/2025.naacl-long.45/ | [
"Paloma Piot",
"Javier Parapar"
] | Hate speech is a harmful form of online expression, often manifesting as derogatory posts. It is a significant risk in digital environments. With the rise of Large Language Models (LLMs), there is concern about their potential to replicate hate speech patterns, given their training on vast amounts of unmoderated intern... | 2025.naacl-long.45 | 10.18653/v1/2025.naacl-long.45 | null | 2410.00775 | title_snapshot |
2025.naacl-long.46 | Babysit A Language Model From Scratch: Interactive Language Learning by Trials and Demonstrations | https://aclanthology.org/2025.naacl-long.46/ | [
"Ziqiao Ma",
"Zekun Wang",
"Joyce Chai"
] | Humans are efficient language learners and inherently social creatures. Our language development is largely shaped by our social interactions, for example, the demonstration and feedback from caregivers. Contrary to human language learning, recent advancements in large language models have primarily adopted a non-inter... | 2025.naacl-long.46 | 10.18653/v1/2025.naacl-long.46 | null | 2405.13828 | title_snapshot |
2025.naacl-long.47 | MoCE: Adaptive Mixture of Contextualization Experts for Byte-based Neural Machine Translation | https://aclanthology.org/2025.naacl-long.47/ | [
"Langlin Huang",
"Mengyu Bu",
"Yang Feng"
] | Byte-based machine translation systems have shown significant potential in massively multilingual settings. Unicode encoding, which maps each character to specific byte(s), eliminates the emergence of unknown words, even in new languages, enabling broad language scalability. However, byte-level tokenization results in ... | 2025.naacl-long.47 | 10.18653/v1/2025.naacl-long.47 | null | 2411.01474 | title_snapshot |
2025.naacl-long.48 | LLM-Human Pipeline for Cultural Grounding of Conversations | https://aclanthology.org/2025.naacl-long.48/ | [
"Rajkumar Pujari",
"Dan Goldwasser"
] | Conversations often adhere to well-understood social norms that vary across cultures. For example, while addressing parents by name is commonplace in the West, it is rare in most Asian cultures. Adherence or violation of such norms often dictates the tenor of conversations. Humans are able to navigate social situations... | 2025.naacl-long.48 | 10.18653/v1/2025.naacl-long.48 | null | 2410.13727 | title_judge |
2025.naacl-long.49 | ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning | https://aclanthology.org/2025.naacl-long.49/ | [
"Vy Vo",
"Lizhen Qu",
"Tao Feng",
"Yuncheng Hua",
"Xiaoxi Kang",
"Songhai Fan",
"Tim Dwyer",
"Lay-Ki Soon",
"Gholamreza Haffari"
] | null | 2025.naacl-long.49 | 10.18653/v1/2025.naacl-long.49 | null | 2502.08148 | title_snapshot |
2025.naacl-long.50 | Unmasking Implicit Bias: Evaluating Persona-Prompted LLM Responses in Power-Disparate Social Scenarios | https://aclanthology.org/2025.naacl-long.50/ | [
"Bryan Chen Zhengyu Tan",
"Roy Ka-Wei Lee"
] | Large language models (LLMs) have demonstrated remarkable capabilities in simulating human behaviour and social intelligence. However, they risk perpetuating societal biases, especially when demographic information is involved. We introduce a novel framework using cosine distance to measure semantic shifts in responses... | 2025.naacl-long.50 | 10.18653/v1/2025.naacl-long.50 | null | 2503.01532 | title_snapshot |
2025.naacl-long.51 | GloCOM: A Short Text Neural Topic Model via Global Clustering Context | https://aclanthology.org/2025.naacl-long.51/ | [
"Quang Duc Nguyen",
"Tung Nguyen",
"Duc Anh Nguyen",
"Linh Ngo Van",
"Sang Dinh",
"Thien Huu Nguyen"
] | Uncovering hidden topics from short texts is challenging for traditional and neural models due to data sparsity, which limits word co-occurrence patterns, and label sparsity, stemming from incomplete reconstruction targets. Although data aggregation offers a potential solution, existing neural topic models often overlo... | 2025.naacl-long.51 | 10.18653/v1/2025.naacl-long.51 | null | 2412.00525 | title_snapshot |
2025.naacl-long.52 | Reversed Attention: On The Gradient Descent Of Attention Layers In GPT | https://aclanthology.org/2025.naacl-long.52/ | [
"Shahar Katz",
"Lior Wolf"
] | The success of Transformer-based Language Models (LMs) stems from their attention mechanism. While this mechanism has been extensively studied in explainability research, particularly through the attention values obtained during the forward pass of LMs, the backward pass of attention has been largely overlooked.In this... | 2025.naacl-long.52 | 10.18653/v1/2025.naacl-long.52 | null | 2412.17019 | title_snapshot |
2025.naacl-long.53 | Self-Harmonized Chain of Thought | https://aclanthology.org/2025.naacl-long.53/ | [
"Ziqi Jin",
"Wei Lu"
] | Chain-of-thought (CoT) prompting has demonstrated the capacity of large language models to perform complex reasoning through intermediate steps. While effective, current CoT methods face challenges: Zero-shot-CoT can lead to reasoning errors, and Few-shot-CoT requires labor-intensive manual demonstrations. Auto-CoT att... | 2025.naacl-long.53 | 10.18653/v1/2025.naacl-long.53 | null | 2409.04057 | title_snapshot |
2025.naacl-long.54 | AnaScore: Understanding Semantic Parallelism in Proportional Analogies | https://aclanthology.org/2025.naacl-long.54/ | [
"Liyan Wang",
"Haotong Wang",
"Yves Lepage"
] | Formulaic criteria for proportional analogies, which capture relational mappings between two ratios of terms, are mainly confined to the formal level. As analogy datasets grow more complex, especially in evaluating the cognitive abilities of Large Language Models (LLMs), assessing parallelism in them becomes increasing... | 2025.naacl-long.54 | 10.18653/v1/2025.naacl-long.54 | null | null | null |
2025.naacl-long.55 | Generating Complex Question Decompositions in the Face of Distribution Shifts | https://aclanthology.org/2025.naacl-long.55/ | [
"Kelvin Han",
"Claire Gardent"
] | Question decomposition has been found to help large language models’ (LLMs) performance on complex question answering (QA) by breaking these questions into simpler sub-questions for answering. Nonetheless, performance on the task remains dominated by supervised approaches, suggesting room for making LLMs better decompo... | 2025.naacl-long.55 | 10.18653/v1/2025.naacl-long.55 | null | null | null |
2025.naacl-long.56 | Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering | https://aclanthology.org/2025.naacl-long.56/ | [
"Yeonjun In",
"Sungchul Kim",
"Ryan A. Rossi",
"Mehrab Tanjim",
"Tong Yu",
"Ritwik Sinha",
"Chanyoung Park"
] | The retrieval augmented generation (RAG) framework addresses an ambiguity in user queries in QA systems by retrieving passages that cover all plausible interpretations and generating comprehensive responses based on the passages. However, our preliminary studies reveal that a single retrieval process often suffers from... | 2025.naacl-long.56 | 10.18653/v1/2025.naacl-long.56 | null | 2409.02361 | title_snapshot |
2025.naacl-long.57 | Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors | https://aclanthology.org/2025.naacl-long.57/ | [
"Kaushal Kumar Maurya",
"Kv Aditya Srivatsa",
"Kseniia Petukhova",
"Ekaterina Kochmar"
] | In this paper, we investigate whether current state-of-the-art large language models (LLMs) are effective as AI tutors and whether they demonstrate pedagogical abilities necessary for good AI tutoring in educational dialogues. Previous efforts towards evaluation have beenlimited to subjective protocols and benchmarks. ... | 2025.naacl-long.57 | 10.18653/v1/2025.naacl-long.57 | null | 2412.09416 | title_snapshot |
2025.naacl-long.58 | Where is the answer? An empirical study of positional bias for parametric knowledge extraction in language model | https://aclanthology.org/2025.naacl-long.58/ | [
"Kuniaki Saito",
"Chen-Yu Lee",
"Kihyuk Sohn",
"Yoshitaka Ushiku"
] | Language model (LM) stores diverse factual knowledge in their parameters, which is learned during self-supervised training on unlabeled documents and is made extractable by instruction-tuning. For knowledge-intensive tasks, it is essential to memorize information in a way that makes it extractable from LM’s parameters ... | 2025.naacl-long.58 | 10.18653/v1/2025.naacl-long.58 | null | 2402.12170 | title_judge |
2025.naacl-long.59 | Evaluating Morphological Compositional Generalization in Large Language Models | https://aclanthology.org/2025.naacl-long.59/ | [
"Mete Ismayilzada",
"Defne Circi",
"Jonne Sälevä",
"Hale Sirin",
"Abdullatif Köksal",
"Bhuwan Dhingra",
"Antoine Bosselut",
"Duygu Ataman",
"Lonneke Van Der Plas"
] | Large language models (LLMs) have demonstrated significant progress in various natural language generation and understanding tasks. However, their linguistic generalization capabilities remain questionable, raising doubts about whether these models learn language similarly to humans. While humans exhibit compositional ... | 2025.naacl-long.59 | 10.18653/v1/2025.naacl-long.59 | null | 2410.12656 | title_snapshot |
2025.naacl-long.60 | Balancing Forget Quality and Model Utility: A Reverse KL-Divergence Knowledge Distillation Approach for Better Unlearning in LLMs | https://aclanthology.org/2025.naacl-long.60/ | [
"Bichen Wang",
"Yuzhe Zi",
"Yixin Sun",
"Yanyan Zhao",
"Bing Qin"
] | As concern for privacy rights has grown and the size of language model training datasets has expanded, research into machine unlearning for large language models (LLMs) has become crucial. Before the era of LLMs, research on machine unlearning mainly focused on classification tasks in small parameter models. However, a... | 2025.naacl-long.60 | 10.18653/v1/2025.naacl-long.60 | null | 2406.01983 | title_judge |
2025.naacl-long.61 | AgentMove: A Large Language Model based Agentic Framework for Zero-shot Next Location Prediction | https://aclanthology.org/2025.naacl-long.61/ | [
"Jie Feng",
"Yuwei Du",
"Jie Zhao",
"Yong Li"
] | Next location prediction plays a crucial role in various real-world applications. Recently, due to the limitation of existing deep learning methods, attempts have been made to apply large language models (LLMs) to zero-shot next location prediction task. However, they directly generate the final output using LLMs witho... | 2025.naacl-long.61 | 10.18653/v1/2025.naacl-long.61 | null | 2408.13986 | title_snapshot |
2025.naacl-long.62 | Embedding derived animacy rankings offer insights into the sources of grammatical animacy | https://aclanthology.org/2025.naacl-long.62/ | [
"Vivian G. Li"
] | In this study, we applied the semantic projection approach to animacy, a feature that has not been previously explored using this method. We compared the relative animacy rankings of nouns denoting animals, humans, objects, and first-, second-, and third-person pronouns, as derived from word embeddings, with rankings d... | 2025.naacl-long.62 | 10.18653/v1/2025.naacl-long.62 | null | null | null |
2025.naacl-long.63 | Generating Long-form Story Using Dynamic Hierarchical Outlining with Memory-Enhancement | https://aclanthology.org/2025.naacl-long.63/ | [
"Qianyue Wang",
"Jinwu Hu",
"Zhengping Li",
"Yufeng Wang",
"Daiyuan Li",
"Yu Hu",
"Mingkui Tan"
] | Long-form story generation task aims to produce coherent and sufficiently lengthy text, essential for applications such as novel writingand interactive storytelling. However, existing methods, including LLMs, rely on rigid outlines or lack macro-level planning, making it difficult to achieve both contextual consistency... | 2025.naacl-long.63 | 10.18653/v1/2025.naacl-long.63 | null | 2412.13575 | title_snapshot |
2025.naacl-long.64 | Little Giants: Synthesizing High-Quality Embedding Data at Scale | https://aclanthology.org/2025.naacl-long.64/ | [
"Haonan Chen",
"Liang Wang",
"Nan Yang",
"Yutao Zhu",
"Ziliang Zhao",
"Furu Wei",
"Zhicheng Dou"
] | Synthetic data generation has become an increasingly popular way of training models without the need for large, manually labeled datasets. For tasks like text embedding, synthetic data offers diverse and scalable training examples, significantly reducing the cost of human annotation. However, most current approaches re... | 2025.naacl-long.64 | 10.18653/v1/2025.naacl-long.64 | null | 2410.18634 | title_snapshot |
2025.naacl-long.65 | Can LLMs Convert Graphs to Text-Attributed Graphs? | https://aclanthology.org/2025.naacl-long.65/ | [
"Zehong Wang",
"Sidney Liu",
"Zheyuan Zhang",
"Tianyi Ma",
"Chuxu Zhang",
"Yanfang Ye"
] | Graphs are ubiquitous structures found in numerous real-world applications, such as drug discovery, recommender systems, and social network analysis. To model graph-structured data, graph neural networks (GNNs) have become a popular tool. However, existing GNN architectures encounter challenges in cross-graph learning ... | 2025.naacl-long.65 | 10.18653/v1/2025.naacl-long.65 | null | 2412.10136 | title_snapshot |
2025.naacl-long.66 | Forest for the Trees: Overarching Prompting Evokes High-Level Reasoning in Large Language Models | https://aclanthology.org/2025.naacl-long.66/ | [
"Haoran Liao",
"Shaohua Hu",
"Zhihao Zhu",
"Hao He",
"Yaohui Jin"
] | Chain-of-thought (CoT) and subsequent methods adopted a deductive paradigm that decomposes the reasoning process, demonstrating remarkable performances across NLP tasks. However, such a paradigm faces the challenge of getting bogged down in low-level semantic details, hindering large language models (LLMs) from correct... | 2025.naacl-long.66 | 10.18653/v1/2025.naacl-long.66 | null | null | null |
2025.naacl-long.67 | On the Role of Speech Data in Reducing Toxicity Detection Bias | https://aclanthology.org/2025.naacl-long.67/ | [
"Samuel Bell",
"Mariano Coria Meglioli",
"Megan Richards",
"Eduardo Sánchez",
"Christophe Ropers",
"Skyler Wang",
"Adina Williams",
"Levent Sagun",
"Marta R. Costa-jussà"
] | Text toxicity detection systems exhibit significant biases, producing disproportionate rates of false positives on samples mentioning demographic groups. But what about toxicity detection in speech? To investigate the extent to which text-based biases are mitigated by speech-based systems, we produce a set of high-qual... | 2025.naacl-long.67 | 10.18653/v1/2025.naacl-long.67 | null | 2411.08135 | title_snapshot |
2025.naacl-long.68 | ITALIC: An Italian Culture-Aware Natural Language Benchmark | https://aclanthology.org/2025.naacl-long.68/ | [
"Andrea Seveso",
"Daniele Potertì",
"Edoardo Federici",
"Mario Mezzanzanica",
"Fabio Mercorio"
] | We present ITALIC, a large-scale benchmark dataset of 10,000 multiple-choice questions designed to evaluate the natural language understanding of the Italian language and culture. ITALIC spans 12 domains, exploiting public tests to score domain experts in real-world scenarios. We detail our data collection process, str... | 2025.naacl-long.68 | 10.18653/v1/2025.naacl-long.68 | null | null | null |
2025.naacl-long.69 | RAP: A Metric for Balancing Repetition and Performance in Open-Source Large Language Models | https://aclanthology.org/2025.naacl-long.69/ | [
"Donghao Huang",
"Thanh-Son Nguyen",
"Fiona Liausvia",
"Zhaoxia Wang"
] | Large Language Models (LLMs) have significantly advanced natural language processing, but content repetition in open-source LLMs remains a critical challenge that adversely affects user experience. The repetition penalty parameter (RPP) aims to mitigate this issue by preventing repeated content generation, but excessiv... | 2025.naacl-long.69 | 10.18653/v1/2025.naacl-long.69 | null | null | null |
2025.naacl-long.70 | Improving Data Annotation for Low-Resource Relation Extraction with Logical Rule-Augmented Collaborative Language Models | https://aclanthology.org/2025.naacl-long.70/ | [
"Xiyang Liu",
"Chunming Hu",
"Richong Zhang",
"Junfan Chen",
"Baowen Xu"
] | Low-resource relation extraction aims to identify semantic relationships between entities using scarce labeled data. Recent studies exploit large language models to recognize relations based on retrieved examplars, yielding promising results. However, the reliability of predictions from these methods is constrained by ... | 2025.naacl-long.70 | 10.18653/v1/2025.naacl-long.70 | null | null | null |
2025.naacl-long.71 | CompAct: Compressed Activations for Memory-Efficient LLM Training | https://aclanthology.org/2025.naacl-long.71/ | [
"Yara Shamshoum",
"Nitzan Hodos",
"Yuval Sieradzki",
"Assaf Schuster"
] | We introduce CompAct, a technique that reduces peak memory utilization on GPU by 25-30% for pretraining and 50% for fine-tuning of LLMs. Peak device memory is a major limiting factor in training LLMs, with various recent works aiming to reduce model memory. However most works don’t target the largest component of alloc... | 2025.naacl-long.71 | 10.18653/v1/2025.naacl-long.71 | null | 2410.15352 | title_snapshot |
2025.naacl-long.72 | Large Language Models Are Cross-Lingual Knowledge-Free Reasoners | https://aclanthology.org/2025.naacl-long.72/ | [
"Peng Hu",
"Sizhe Liu",
"Changjiang Gao",
"Xin Huang",
"Xue Han",
"Junlan Feng",
"Chao Deng",
"Shujian Huang"
] | Large Language Models have demonstrated impressive reasoning capabilities across multiple languages. However, the relationship between capabilities in different languages is less explored. In this work, we decompose the process of reasoning tasks into two separated components: knowledge retrieval and knowledge-free rea... | 2025.naacl-long.72 | 10.18653/v1/2025.naacl-long.72 | null | 2406.16655 | title_snapshot |
2025.naacl-long.73 | What Did I Do Wrong? Quantifying LLMs’ Sensitivity and Consistency to Prompt Engineering | https://aclanthology.org/2025.naacl-long.73/ | [
"Federico Errica",
"Davide Sanvito",
"Giuseppe Siracusano",
"Roberto Bifulco"
] | Large Language Models (LLMs) changed the way we design and interact with software systems. Their ability to process and extract information from text has drastically improved productivity in a number of routine tasks. Developers that want to include these models in their software stack, however, face a dreadful challen... | 2025.naacl-long.73 | 10.18653/v1/2025.naacl-long.73 | null | 2406.12334 | title_snapshot |
2025.naacl-long.74 | Detect, Disambiguate, and Translate: On-Demand Visual Reasoning for Multimodal Machine Translation with Large Vision-Language Models | https://aclanthology.org/2025.naacl-long.74/ | [
"Danyang Liu",
"Fanjie Kong",
"Xiaohang Sun",
"Dhruva Patil",
"Avijit Vajpayee",
"Zhu Liu",
"Vimal Bhat",
"Najmeh Sadoughi"
] | Multimodal machine translation (MMT) aims to leverage additional modalities to assist in language translation. With limited parallel data, current MMT systems rely heavily on monolingual English captioning data. These systems face three key issues: they often overlook that visual signals are unnecessary in many cases, ... | 2025.naacl-long.74 | 10.18653/v1/2025.naacl-long.74 | null | null | null |
2025.naacl-long.75 | Mitigating Hallucinations in Multi-modal Large Language Models via Image Token Attention-Guided Decoding | https://aclanthology.org/2025.naacl-long.75/ | [
"Xinhao Xu",
"Hui Chen",
"Mengyao Lyu",
"Sicheng Zhao",
"Yizhe Xiong",
"Zijia Lin",
"Jungong Han",
"Guiguang Ding"
] | Multi-modal large language models (MLLMs) integrate the inherent text generation capabilities of large language models with an understanding of other modalities, promising wide applications in open-ended tasks. Despite their success, they often generate plausible but incorrect content. This phenomenon, known as halluci... | 2025.naacl-long.75 | 10.18653/v1/2025.naacl-long.75 | null | null | null |
2025.naacl-long.76 | A Multi-modal Large Language Model with Graph-of-Thought for Effective Recommendation | https://aclanthology.org/2025.naacl-long.76/ | [
"Zixuan Yi",
"Iadh Ounis"
] | Chain-of-Thought (CoT) prompting has been shown to be effective in guiding Large Language Models (LLMs) to decompose complex tasks into multiple intermediate steps, and constructing a rational reasoning chain for inferring answers. However, the linear nature of CoT falls short from enabling LLMs to effectively handle g... | 2025.naacl-long.76 | 10.18653/v1/2025.naacl-long.76 | null | null | null |
2025.naacl-long.77 | Investigating Human Values in Online Communities | https://aclanthology.org/2025.naacl-long.77/ | [
"Nadav Borenstein",
"Arnav Arora",
"Lucie-Aimée Kaffee",
"Isabelle Augenstein"
] | Studying human values is instrumental for cross-cultural research, enabling a better understanding of preferences and behaviour of society at large and communities therein. To study the dynamics of communities online, we propose a method to computationally analyse values present on Reddit. Our method allows analysis at... | 2025.naacl-long.77 | 10.18653/v1/2025.naacl-long.77 | null | 2402.14177 | title_snapshot |
2025.naacl-long.78 | Pointwise Mutual Information as a Performance Gauge for Retrieval-Augmented Generation | https://aclanthology.org/2025.naacl-long.78/ | [
"Tianyu Liu",
"Jirui Qi",
"Paul He",
"Arianna Bisazza",
"Mrinmaya Sachan",
"Ryan Cotterell"
] | Recent work suggests that large language models enhanced with retrieval-augmented generation are easily influenced by the order in which the retrieved documents are presented to the model when solving tasks such as question answering (QA).However, there is no method to date that exploits this phenomenon to improve gene... | 2025.naacl-long.78 | 10.18653/v1/2025.naacl-long.78 | null | 2411.07773 | title_snapshot |
2025.naacl-long.79 | MATO: A Model-Agnostic Training Optimization for Aspect Sentiment Triplet Extraction | https://aclanthology.org/2025.naacl-long.79/ | [
"Shaopeng Tang",
"Lin Li",
"Xiaohui Tao",
"Leqi Zhong",
"Qing Xie"
] | As an important fine-grained sentiment analysis task, aspect sentiment triplet extraction (ASTE) aims to identify three elements, i.e., aspect, opinion and sentiment polarity as a triplet. Advanced ASTE researches have mostly explored triplet-wise ability to achieve superior improvement. However, existing models with s... | 2025.naacl-long.79 | 10.18653/v1/2025.naacl-long.79 | null | null | null |
2025.naacl-long.80 | Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts | https://aclanthology.org/2025.naacl-long.80/ | [
"Tong Zhu",
"Daize Dong",
"Xiaoye Qu",
"Jiacheng Ruan",
"Wenliang Chen",
"Yu Cheng"
] | Mixture-of-Experts (MoE) models have shown remarkable capability in instruction tuning, especially when the number of tasks scales. However, previous methods simply merge all training tasks (e.g. creative writing, coding, and mathematics) and apply fixed sampling weights, without considering the importance of different... | 2025.naacl-long.80 | 10.18653/v1/2025.naacl-long.80 | null | 2406.11256 | title_snapshot |
2025.naacl-long.81 | EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamics | https://aclanthology.org/2025.naacl-long.81/ | [
"Chenwei Wan",
"Matthieu Labeau",
"Chloé Clavel"
] | Designing emotionally intelligent conversational systems to provide comfort and advice to people experiencing distress is a compelling area of research. Recently, with advancements in large language models (LLMs), end-to-end dialogue agents without explicit strategy prediction steps have become prevalent. However, impl... | 2025.naacl-long.81 | 10.18653/v1/2025.naacl-long.81 | null | 2408.08782 | title_snapshot |
2025.naacl-long.82 | ReasVQA: Advancing VideoQA with Imperfect Reasoning Process | https://aclanthology.org/2025.naacl-long.82/ | [
"Jianxin Liang",
"Xiaojun Meng",
"Huishuai Zhang",
"Yueqian Wang",
"Jiansheng Wei",
"Dongyan Zhao"
] | null | 2025.naacl-long.82 | 10.18653/v1/2025.naacl-long.82 | null | 2501.13536 | title_snapshot |
2025.naacl-long.83 | Divergent Thoughts toward One Goal: LLM-based Multi-Agent Collaboration System for Electronic Design Automation | https://aclanthology.org/2025.naacl-long.83/ | [
"Haoyuan Wu",
"Haisheng Zheng",
"Zhuolun He",
"Bei Yu"
] | Recently, with the development of tool-calling capabilities in large language models (LLMs), these models have demonstrated significant potential for automating electronic design automation (EDA) flows by interacting with EDA tool APIs via EDA scripts.However, considering the limited understanding of EDA tools, LLMs fa... | 2025.naacl-long.83 | 10.18653/v1/2025.naacl-long.83 | null | 2502.10857 | title_snapshot |
2025.naacl-long.84 | A Survey of QUD Models for Discourse Processing | https://aclanthology.org/2025.naacl-long.84/ | [
"Yingxue Fu"
] | Question Under Discussion (QUD), which is originally a linguistic analytic framework, gains increasing attention in the community of natural language processing over the years. Various models have been proposed for implementing QUD for discourse processing. This survey summarizes these models, with a focus on applicati... | 2025.naacl-long.84 | 10.18653/v1/2025.naacl-long.84 | null | 2502.15573 | title_snapshot |
2025.naacl-long.85 | SafetyQuizzer: Timely and Dynamic Evaluation on the Safety of LLMs | https://aclanthology.org/2025.naacl-long.85/ | [
"Zhichao Shi",
"Shaoling Jing",
"Yi Cheng",
"Hao Zhang",
"Yuanzhuo Wang",
"Jie Zhang",
"Huawei Shen",
"Xueqi Cheng"
] | With the expansion of the application of Large Language Models (LLMs), concerns about their safety have grown among researchers. Numerous studies have demonstrated the potential risks of LLMs generating harmful content and have proposed various safety assessment benchmarks to evaluate these risks. However, the evaluati... | 2025.naacl-long.85 | 10.18653/v1/2025.naacl-long.85 | null | null | null |
2025.naacl-long.86 | Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory | https://aclanthology.org/2025.naacl-long.86/ | [
"Haoran Li",
"Wei Fan",
"Yulin Chen",
"Cheng Jiayang",
"Tianshu Chu",
"Xuebing Zhou",
"Peizhao Hu",
"Yangqiu Song"
] | Privacy research has attracted wide attention as individuals worry that their private data can be easily leaked during interactions with smart devices, social platforms, and AI applications. Existing works mostly consider privacy attacks and defenses on various sub-fields. Within each field, various privacy attacks and... | 2025.naacl-long.86 | 10.18653/v1/2025.naacl-long.86 | null | 2408.10053 | title_snapshot |
2025.naacl-long.87 | Investigating the (De)Composition Capabilities of Large Language Models in Natural-to-Formal Language Conversion | https://aclanthology.org/2025.naacl-long.87/ | [
"Ziyao Xu",
"Houfeng Wang"
] | Humans have strong capabilities of decomposition and composition in natural-to-formal language conversion (N2F) when faced with an unfamiliar formal language, and can easily cope with compositional gaps and counter-intuitive symbolic names. To investigate whether large language models (LLMs) have this set of basic capa... | 2025.naacl-long.87 | 10.18653/v1/2025.naacl-long.87 | null | 2501.14649 | title_snapshot |
2025.naacl-long.88 | Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring | https://aclanthology.org/2025.naacl-long.88/ | [
"Honglin Mu",
"Han He",
"Yuxin Zhou",
"Yunlong Feng",
"Yang Xu",
"Libo Qin",
"Xiaoming Shi",
"Zeming Liu",
"Xudong Han",
"Qi Shi",
"Qingfu Zhu",
"Wanxiang Che"
] | Large language model (LLM) safety is a critical issue, with numerous studies employing red team testing to enhance model security. Among these, jailbreak methods explore potential vulnerabilities by crafting malicious prompts that induce model outputs contrary to safety alignments. Existing black-box jailbreak methods ... | 2025.naacl-long.88 | 10.18653/v1/2025.naacl-long.88 | null | 2410.21083 | title_snapshot |
2025.naacl-long.89 | VividMed: Vision Language Model with Versatile Visual Grounding for Medicine | https://aclanthology.org/2025.naacl-long.89/ | [
"Lingxiao Luo",
"Bingda Tang",
"Xuanzhong Chen",
"Rong Han",
"Ting Chen"
] | Recent advancements in Vision Language Models (VLMs) have demonstrated remarkable promise in generating visually grounded responses. However, their application in the medical domain is hindered by unique challenges. For instance, most VLMs rely on a single method of visual grounding, whereas complex medical tasks deman... | 2025.naacl-long.89 | 10.18653/v1/2025.naacl-long.89 | null | 2410.12694 | title_snapshot |
2025.naacl-long.90 | Mixture of Multimodal Adapters for Sentiment Analysis | https://aclanthology.org/2025.naacl-long.90/ | [
"Kezhou Chen",
"Shuo Wang",
"Huixia Ben",
"Shengeng Tang",
"Yanbin Hao"
] | Pre-trained language model (PLM) have achieved great success in text sentiment analysis. However, in practical applications, sentiment is not only conveyed through language but also hidden in other modalities. Therefore, multimodal sentiment analysis (MSA) has attracted increasing research interest. Compared to text se... | 2025.naacl-long.90 | 10.18653/v1/2025.naacl-long.90 | null | null | null |
2025.naacl-long.91 | The Impact of Inference Acceleration on Bias of LLMs | https://aclanthology.org/2025.naacl-long.91/ | [
"Elisabeth Kirsten",
"Ivan Habernal",
"Vedant Nanda",
"Muhammad Bilal Zafar"
] | Last few years have seen unprecedented advances in capabilities of Large Language Models (LLMs). These advancements promise to benefit a vast array of application domains. However, due to their immense size, performing inference with LLMs is both costly and slow. Consequently, a plethora of recent work has proposed str... | 2025.naacl-long.91 | 10.18653/v1/2025.naacl-long.91 | null | 2410.22118 | title_snapshot |
2025.naacl-long.92 | AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages | https://aclanthology.org/2025.naacl-long.92/ | [
"Shamsuddeen Hassan Muhammad",
"Idris Abdulmumin",
"Abinew Ali Ayele",
"David Ifeoluwa Adelani",
"Ibrahim Said Ahmad",
"Saminu Mohammad Aliyu",
"Paul Röttger",
"Abigail Oppong",
"Andiswa Bukula",
"Chiamaka Ijeoma Chukwuneke",
"Ebrahim Chekol Jibril",
"Elyas Abdi Ismail",
"Esubalew Alemneh",
... | Hate speech and abusive language are global phenomena that need socio-cultural background knowledge to be understood, identified, and moderated. However, in many regions of the Global South, there have been several documented occurrences of (1) absence of moderation and (2) censorship due to the reliance on keyword spo... | 2025.naacl-long.92 | 10.18653/v1/2025.naacl-long.92 | null | 2501.08284 | title_snapshot |
2025.naacl-long.93 | Revealing the Barriers of Language Agents in Planning | https://aclanthology.org/2025.naacl-long.93/ | [
"Jian Xie",
"Kexun Zhang",
"Jiangjie Chen",
"Siyu Yuan",
"Kai Zhang",
"Yikai Zhang",
"Lei Li",
"Yanghua Xiao"
] | Autonomous planning has been an ongoing pursuit since the inception of artificial intelligence. Based on curated problem solvers, early planning agents could deliver precise solutions for specific tasks but lacked generalization. The emergence of large language models (LLMs) and their powerful reasoning capabilities ha... | 2025.naacl-long.93 | 10.18653/v1/2025.naacl-long.93 | null | 2410.12409 | title_snapshot |
2025.naacl-long.94 | You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL | https://aclanthology.org/2025.naacl-long.94/ | [
"Hideo Kobayashi",
"Wuwei Lan",
"Peng Shi",
"Shuaichen Chang",
"Jiang Guo",
"Henghui Zhu",
"Zhiguo Wang",
"Patrick Ng"
] | While significant progress has been made on the text-to-SQL task, recent solutions repeatedly encode the same database schema for every question, resulting in unnecessary high inference cost and often overlooking crucial database knowledge. To address these issues, we propose You Only Read Once (YORO), a novel paradigm... | 2025.naacl-long.94 | 10.18653/v1/2025.naacl-long.94 | null | 2409.12172 | title_snapshot |
2025.naacl-long.95 | Option Symbol Matters: Investigating and Mitigating Multiple-Choice Option Symbol Bias of Large Language Models | https://aclanthology.org/2025.naacl-long.95/ | [
"Zhen Yang",
"Ping Jian",
"Chengzhi Li"
] | Multiple-Choice Question Answering (MCQA) is a widely used task in the evaluation of Large Language Models (LLMs). In this work, we reveal that current LLMs’ performance in MCQA could be heavily influenced by the choice of option symbol sets, due to the option symbol bias. That is, when altering only the option symbols... | 2025.naacl-long.95 | 10.18653/v1/2025.naacl-long.95 | null | null | null |
2025.naacl-long.96 | DAWN-ICL: Strategic Planning of Problem-solving Trajectories for Zero-Shot In-Context Learning | https://aclanthology.org/2025.naacl-long.96/ | [
"Xinyu Tang",
"Xiaolei Wang",
"Wayne Xin Zhao",
"Ji-Rong Wen"
] | Zero-shot in-context learning (ZS-ICL) aims to conduct in-context learning (ICL) without using human-annotated demonstrations.Existing ZS-ICL methods either use large language models (LLMs) to generate (input, label) pairs as pseudo-demonstrations or leverage historical pseudo-demonstrations to help solve the current p... | 2025.naacl-long.96 | 10.18653/v1/2025.naacl-long.96 | null | 2410.20215 | title_snapshot |
2025.naacl-long.97 | LLaSA: Large Language and Structured Data Assistant | https://aclanthology.org/2025.naacl-long.97/ | [
"Yao Xu",
"Shizhu He",
"Jiabei Chen",
"Xiangrong Zeng",
"Bingning Wang",
"Jun Zhao",
"Kang Liu"
] | null | 2025.naacl-long.97 | 10.18653/v1/2025.naacl-long.97 | null | 2411.14460 | title_snapshot |
2025.naacl-long.98 | Towards Efficient and Multifaceted Computer-assisted Pronunciation Training Leveraging Hierarchical Selective State Space Model and Decoupled Cross-entropy Loss | https://aclanthology.org/2025.naacl-long.98/ | [
"Fu-An Chao",
"Berlin Chen"
] | Prior efforts in building computer-assisted pronunciation training (CAPT) systems often treat automatic pronunciation assessment (APA) and mispronunciation detection and diagnosis (MDD) as separate fronts: the former aims to provide multiple pronunciation aspect scores across diverse linguistic levels, while the latter... | 2025.naacl-long.98 | 10.18653/v1/2025.naacl-long.98 | null | 2502.07575 | title_snapshot |
2025.naacl-long.99 | Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models | https://aclanthology.org/2025.naacl-long.99/ | [
"Abhilasha Ravichander",
"Jillian Fisher",
"Taylor Sorensen",
"Ximing Lu",
"Maria Antoniak",
"Bill Yuchen Lin",
"Niloofar Mireshghallah",
"Chandra Bhagavatula",
"Yejin Choi"
] | High-quality training data has proven crucial for developing performant large language models (LLMs). However, commercial LLM providers disclose few, if any, details about the data used for training. This lack of transparency creates multiple challenges: it limits external oversight and inspection of LLMs for issues su... | 2025.naacl-long.99 | 10.18653/v1/2025.naacl-long.99 | null | 2503.12072 | title_snapshot |
2025.naacl-long.100 | An Interpretable and Crosslingual Method for Evaluating Second-Language Dialogues | https://aclanthology.org/2025.naacl-long.100/ | [
"Rena Gao",
"Jingxuan Wu",
"Xuetong Wu",
"Carsten Roever",
"Jing Wu",
"Long Lv",
"Jey Han Lau"
] | We analyse the cross-lingual transferability of a dialogue evaluation framework that assesses the relationships between micro-level linguistic features (e.g. backchannels) and macro-level interactivity labels (e.g. topic management), originally designed for English-as-a-second-language dialogues. To this end, we develo... | 2025.naacl-long.100 | 10.18653/v1/2025.naacl-long.100 | null | 2408.16518 | title_snapshot |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- -