Datasets:

yufan
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top-conference-papers

title stringlengths 21 144	authors stringlengths 11 658	venue_short stringclasses 1 value	abstract stringlengths 0 1.87k	decision stringclasses 2 values	arxiv_url stringlengths 32 32 ⌀	source_url stringlengths 41 43	source_type stringclasses 1 value
Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models	Zhengxin Zhang, Dan Zhao, Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Qing Li, Yong Jiang, Zhihao Jia	ACL2024	Finetuning large language models (LLMs) has been empirically effective on a variety of downstream tasks. Existing approaches to finetuning an LLM either focus on parameter-efficient finetuning, which only updates a small number of trainable parameters, or attempt to reduce the memory footprint during the training phase...	long	https://arxiv.org/abs/2401.07159	https://aclanthology.org/2024.acl-long.1/	anthology
Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances	Hanlei Zhang, Hua Xu, Fei Long, Xin Wang, Kai Gao	ACL2024	Discovering the semantics of multimodal utterances is essential for understanding human language and enhancing human-machine interactions. Existing methods manifest limitations in leveraging nonverbal information for discerning complex semantics in unsupervised scenarios. This paper introduces a novel unsupervised mult...	long	https://arxiv.org/abs/2405.12775	https://aclanthology.org/2024.acl-long.2/	anthology
MAGE: Machine-generated Text Detection in the Wild	Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi, Yue Zhang	ACL2024	Large language models (LLMs) have achieved human-level text generation, emphasizing the need for effective deepfake text detection to mitigate risks like the spread of fake news and plagiarism. Existing research has been constrained by evaluating detection methods o specific domains or particular language models. In pr...	long	https://arxiv.org/abs/2305.13242	https://aclanthology.org/2024.acl-long.3/	anthology
PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models	Haoran Li, Dadi Guo, Donghao Li, Wei Fan, Qi Hu, Xin Liu, Chunkit Chan, Duanyi Yao, Yuan Yao, Yangqiu Song	ACL2024	The rapid development of language models (LMs) brings unprecedented accessibility and usage for both models and users. On the one hand, powerful LMs achieve state-of-the-art performance over numerous downstream NLP tasks. On the other hand, more and more attention is paid to unrestricted model accesses that may bring m...	long	https://arxiv.org/abs/2311.04044	https://aclanthology.org/2024.acl-long.4/	anthology
GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators	Yuchen Hu, Chen Chen, Chao-Han Huck Yang, Ruizhe Li, Dong Zhang, Zhehuai Chen, Eng Siong Chng	ACL2024	Recent advances in large language models (LLMs) have stepped forward the development of multilingual speech and machine translation by its reduced representation errors and incorporated external knowledge. However, both translation tasks typically utilize beam search decoding and top-1 hypothesis selection for inferenc...	long	https://arxiv.org/abs/2402.06894	https://aclanthology.org/2024.acl-long.5/	anthology
BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation	DaYou Du, Yijia Zhang, Shijie Cao, Jiaqi Guo, Ting Cao, Xiaowen Chu, Ningyi Xu	ACL2024	The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment challenges. Weight quantization has emerged as a widely embraced solution to reduce memory and computational demands. This paper introduces BitDistiller, a framework tha...	long	https://arxiv.org/abs/2402.10631	https://aclanthology.org/2024.acl-long.7/	anthology
A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation	Kai Chen, Ye Wang, Yitong Li, Aiping Li, Han Yu, Xin Song	ACL2024	Temporal knowledge graph (TKG) reasoning has two settings: interpolation reasoning and extrapolation reasoning. Both of them draw plenty of research interest and have great significance. Methods of the former de-emphasize the temporal correlations among facts sequences, while methods of the latter require strict chrono...	long	https://arxiv.org/abs/2405.18106	https://aclanthology.org/2024.acl-long.8/	anthology
Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation	Shicheng Xu, Liang Pang, Mo Yu, Fandong Meng, Huawei Shen, Xueqi Cheng, Jie Zhou	ACL2024	Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating additional information from retrieval. However, studies have shown that LLMs still face challenges in effectively using the retrieved information, even ignore it or be misled by it. The key reason is that the training of LLMs do...	long	https://arxiv.org/abs/2402.18150	https://aclanthology.org/2024.acl-long.9/	anthology
CSCD-NS: a Chinese Spelling Check Dataset for Native Speakers	Yong Hu, Fandong Meng, Jie Zhou	ACL2024	In this paper, we present CSCD-NS, the first Chinese spelling check (CSC) dataset designed for native speakers, containing 40,000 samples from a Chinese social platform. Compared with existing CSC datasets aimed at Chinese learners, CSCD-NS is ten times larger in scale and exhibits a distinct error distribution, with a...	long	https://arxiv.org/abs/2211.08788	https://aclanthology.org/2024.acl-long.10/	anthology
Evaluating Dynamic Topic Models	Charu Karakkaparambil James, Mayank Nagda, Nooshin Haji Ghassemi, Marius Kloft, Sophie Fellenz	ACL2024	There is a lack of quantitative measures to evaluate the progression of topics through time in dynamic topic models (DTMs). Filling this gap, we propose a novel evaluation measure for DTMs that analyzes the changes in the quality of each topic over time. Additionally, we propose an extension combining topic quality wit...	long	https://arxiv.org/abs/2309.08627	https://aclanthology.org/2024.acl-long.11/	anthology
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition	Guanting Dong, Hongyi Yuan, Keming Lu, Chengpeng Li, Mingfeng Xue, Dayiheng Liu, Wei Wang, Zheng Yuan, Chang Zhou, Jingren Zhou	ACL2024	Large language models (LLMs) with enormous pre-training tokens and parameters emerge diverse abilities, including math reasoning, codegeneration, and instruction following. These abilities are further enhanced by supervised fine-tuning (SFT). While the open-source community has explored ad-hoc SFT for enhancing individ...	long	https://arxiv.org/abs/2310.05492	https://aclanthology.org/2024.acl-long.12/	anthology
Through the Lens of Split Vote: Exploring Disagreement, Difficulty and Calibration in Legal Case Outcome Classification	Shanshan Xu, Santosh T.y.s.s, Oana Ichim, Barbara Plank, Matthias Grabmair	ACL2024	In legal decisions, split votes (SV) occur when judges cannot reach a unanimous decision, posing a difficulty for lawyers who must navigate diverse legal arguments and opinions. In high-stakes domains, %as human-AI interaction systems become increasingly important, understanding the alignment of perceived difficulty be...	long	https://arxiv.org/abs/2402.07214	https://aclanthology.org/2024.acl-long.13/	anthology
Inference to the Best Explanation in Large Language Models	Dhairya Dalal, Marco Valentino, Andre Freitas, Paul Buitelaar	ACL2024	While Large Language Models (LLMs) have found success in real-world applications, their underlying explanatory process is still poorly understood. This paper proposes IBE-Eval, a framework inspired by philosophical accounts on Inference to the Best Explanation (IBE) to advance the interpretation and evaluation of LLMs’...	long	https://arxiv.org/abs/2402.10767	https://aclanthology.org/2024.acl-long.14/	anthology
A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus	Eduard Poesina, Cornelia Caragea, Radu Tudor Ionescu	ACL2024	Natural language inference (NLI), the task of recognizing the entailment relationship in sentence pairs, is an actively studied topic serving as a proxy for natural language understanding. Despite the relevance of the task in building conversational agents and improving text classification, machine translation and othe...	long	https://arxiv.org/abs/2405.11877	https://aclanthology.org/2024.acl-long.15/	anthology
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering	Xiusi Chen, Jyun-Yu Jiang, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Wei Wang	ACL2024	Recent advances in few-shot question answering (QA) mostly rely on the power of pre-trained large language models (LLMs) and fine-tuning in specific settings. Although the pre-training stage has already equipped LLMs with powerful reasoning capabilities, LLMs still need to be fine-tuned to adapt to specific domains to ...	long	https://arxiv.org/abs/2310.05007	https://aclanthology.org/2024.acl-long.16/	anthology
SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs	Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Dong Yu, Fei Liu	ACL2024	Large language models hold significant potential for integrating various data types, such as text documents and database records, for advanced analytics. However, blending text and numerical data presents substantial challenges. LLMs need to process and cross-reference entities and numbers, handle data inconsistencies ...	long	https://arxiv.org/abs/2402.10979	https://aclanthology.org/2024.acl-long.17/	anthology
SciMON: Scientific Inspiration Machines Optimized for Novelty	Qingyun Wang, Doug Downey, Heng Ji, Tom Hope	ACL2024	We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature. Work on literature-based hypothesis generation has traditionally focused on binary link prediction—severely limiting the expressivity of hypotheses. This line of work also does not focus on optim...	long	https://arxiv.org/abs/2305.14259	https://aclanthology.org/2024.acl-long.18/	anthology
Expedited Training of Visual Conditioned Language Generation via Redundancy Reduction	Yiren Jian, Tingkai Liu, Yunzhe Tao, Chunhui Zhang, Soroush Vosoughi, Hongxia Yang	ACL2024	We introduce \text{EVL}_{\text{Gen}}, a streamlined framework designed for the pre-training of visually conditioned language generation models with high computational demands, utilizing frozen pre-trained large language models (LLMs). The conventional approach in vision-language pre-training (VLP) typically involves a ...	long	https://arxiv.org/abs/2310.03291	https://aclanthology.org/2024.acl-long.19/	anthology
Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models	Abhishek Kumar, Robert Morabito, Sanzhar Umbet, Jad Kabbara, Ali Emami	ACL2024	As the use of Large Language Models (LLMs) becomes more widespread, understanding their self-evaluation of confidence in generated responses becomes increasingly important as it is integral to the reliability of the output of these models. We introduce the concept of Confidence-Probability Alignment, that connects an L...	long	https://arxiv.org/abs/2405.16282	https://aclanthology.org/2024.acl-long.20/	anthology
Retrieval-Augmented Multilingual Knowledge Editing	Weixuan Wang, Barry Haddow, Alexandra Birch	ACL2024	Knowledge represented in Large Language Models (LLMs) is quite often incorrect and can also become obsolete over time. Updating knowledge via fine-tuning is computationally resource-hungry and not reliable, and so knowledge editing (KE) has developed as an effective and economical alternative to inject new knowledge or...	long	https://arxiv.org/abs/2312.13040	https://aclanthology.org/2024.acl-long.21/	anthology
Picturing Ambiguity: A Visual Twist on the Winograd Schema Challenge	Brendan Park, Madeline Janecek, Naser Ezzati-Jivan, Yifeng Li, Ali Emami	ACL2024	Large Language Models (LLMs) have demonstrated remarkable success in tasks like the Winograd Schema Challenge (WSC), showcasing advanced textual common-sense reasoning. However, applying this reasoning to multimodal domains, where understanding text and images together is essential, remains a substantial challenge. To ...	long	https://arxiv.org/abs/2405.16277	https://aclanthology.org/2024.acl-long.22/	anthology
Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models	Abhishek Kumar, Sarfaroz Yunusov, Ali Emami	ACL2024	Research on Large Language Models (LLMs) has often neglected subtle biases that, although less apparent, can significantly influence the models’ outputs toward particular social narratives. This study addresses two such biases within LLMs: representative bias, which denotes a tendency of LLMs to generate outputs that m...	long	https://arxiv.org/abs/2405.14555	https://aclanthology.org/2024.acl-long.23/	anthology
Framing in the Presence of Supporting Data: A Case Study in U.S. Economic News	Alexandria Leto, Elliot Pickens, Coen Needell, David Rothschild, Maria Leonor Pacheco	ACL2024	The mainstream media has much leeway in what it chooses to cover and how it covers it. These choices have real-world consequences on what people know and their subsequent behaviors. However, the lack of objective measures to evaluate editorial choices makes research in this area particularly difficult. In this paper, w...	long	https://arxiv.org/abs/2402.14224	https://aclanthology.org/2024.acl-long.24/	anthology
Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences	Xiyao Wang, Yuhang Zhou, Xiaoyu Liu, Hongjin Lu, Yuancheng Xu, Feihong He, Jaehong Yoon, Taixi Lu, Fuxiao Liu, Gedas Bertasius, Mohit Bansal, Huaxiu Yao, Furong Huang	ACL2024	Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks. However, current MLLM benchmarks are predominantly designed to evaluate reasoning based on static information about a single image, and the ability of modern MLLMs to extrapolate from image sequences, ...	long	https://arxiv.org/abs/2401.10529	https://aclanthology.org/2024.acl-long.25/	anthology
TTM-RE: Memory-Augmented Document-Level Relation Extraction	Chufan Gao, Xuan Wang, Jimeng Sun	ACL2024	Document-level relation extraction aims to categorize the association between any two entities within a document.We find that previous methods for document-level relation extraction are ineffective in exploiting the full potential of large amounts of training data with varied noise levels. For example, in the ReDocRED ...	long	https://arxiv.org/abs/2406.05906	https://aclanthology.org/2024.acl-long.26/	anthology
Answer is All You Need: Instruction-following Text Embedding via Answering the Question	Letian Peng, Yuwei Zhang, Zilong Wang, Jayanth Srinivasa, Gaowen Liu, Zihan Wang, Jingbo Shang	ACL2024	This work aims to build a text embedder that can capture characteristics of texts specified by user instructions clarifying the similarity criterion. While previous methods improve general task awareness by injecting the instruction information into encoding, they fail to be sensitive to clearer criteria like “evaluate...	long	https://arxiv.org/abs/2402.09642	https://aclanthology.org/2024.acl-long.27/	anthology
Explore Spurious Correlations at the Concept Level in Language Models for Text Classification	Yuhang Zhou, Paiheng Xu, Xiaoyu Liu, Bang An, Wei Ai, Furong Huang	ACL2024	Language models (LMs) have achieved notable success in numerous NLP tasks, employing both fine-tuning and in-context learning (ICL) methods. While language models demonstrate exceptional performance, they face robustness challenges due to spurious correlations arising from imbalanced label distributions in training dat...	long	https://arxiv.org/abs/2311.08648	https://aclanthology.org/2024.acl-long.28/	anthology
Every Answer Matters: Evaluating Commonsense with Probabilistic Measures	Qi Cheng, Michael Boratko, Pranay Kumar Yelugam, Tim O’Gorman, Nalini Singh, Andrew McCallum, Xiang Lorraine Li	ACL2024	Large language models have demonstrated impressive performance on commonsense tasks; however, these tasks are often posed as multiple-choice questions, allowing models to exploit systematic biases. Commonsense is also inherently probabilistic with multiple correct answers. The purpose of “boiling water” could be making...	long	https://arxiv.org/abs/2406.04145	https://aclanthology.org/2024.acl-long.29/	anthology
GradSafe: Detecting Jailbreak Prompts for LLMs via Safety-Critical Gradient Analysis	Yueqi Xie, Minghong Fang, Renjie Pi, Neil Gong	ACL2024	Large Language Models (LLMs) face threats from jailbreak prompts. Existing methods for detecting jailbreak prompts are primarily online moderation APIs or finetuned LLMs. These strategies, however, often require extensive and resource-intensive data collection and training processes. In this study, we propose GradSafe,...	long	https://arxiv.org/abs/2402.13494	https://aclanthology.org/2024.acl-long.30/	anthology
An Information-Theoretic Approach to Analyze NLP Classification Tasks	Luran Wang, Mark Gales, Vatsal Raina	ACL2024	Understanding the contribution of the inputs on the output is useful across many tasks. This work provides an information-theoretic framework to analyse the influence of inputs for text classification tasks. Natural language processing (NLP) tasks take either a single or multiple text elements to predict an output vari...	long	https://arxiv.org/abs/2402.00978	https://aclanthology.org/2024.acl-long.32/	anthology
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders	Yuwei Zhang, Siffi Singh, Sailik Sengupta, Igor Shalyminov, Hang Su, Hwanjun Song, Saab Mansour	ACL2024	Conversational systems often rely on embedding models for intent classification and intent clustering tasks. The advent of Large Language Models (LLMs), which enable instructional embeddings allowing one to adjust semantics over the embedding space using prompts, are being viewed as a panacea for these downstream conve...	long	https://arxiv.org/abs/2403.04314	https://aclanthology.org/2024.acl-long.33/	anthology
Wav2Gloss: Generating Interlinear Glossed Text from Speech	Taiqi He, Kwanghee Choi, Lindia Tjuatja, Nathaniel Robinson, Jiatong Shi, Shinji Watanabe, Graham Neubig, David Mortensen, Lori Levin	ACL2024	Thousands of the world’s languages are in danger of extinction—a tremendous threat to cultural identities and human language diversity. Interlinear Glossed Text (IGT) is a form of linguistic annotation that can support documentation and resource creation for these languages’ communities. IGT typically consists of (1) t...	long	https://arxiv.org/abs/2403.13169	https://aclanthology.org/2024.acl-long.34/	anthology
Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification	Yibo Hu, Erick Skorupa Parolin, Latifur Khan, Patrick Brandt, Javier Osorio, Vito D’Orazio	ACL2024	Is it possible accurately classify political relations within evolving event ontologies without extensive annotations? This study investigates zero-shot learning methods that use expert knowledge from existing annotation codebook, and evaluates the performance of advanced ChatGPT (GPT-3.5/4) and a natural language infe...	long	https://arxiv.org/abs/2308.07876	https://aclanthology.org/2024.acl-long.35/	anthology
SPOR: A Comprehensive and Practical Evaluation Method for Compositional Generalization in Data-to-Text Generation	Ziyao Xu, Houfeng Wang	ACL2024	Compositional generalization is an important ability of language models and has many different manifestations. For data-to-text generation, previous research on this ability is limited to a single manifestation called Systematicity and lacks consideration of large language models (LLMs), which cannot fully cover practi...	long	https://arxiv.org/abs/2405.10650	https://aclanthology.org/2024.acl-long.36/	anthology
OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following	Haochen Shi, Zhiyuan Sun, Xingdi Yuan, Marc-Alexandre Côté, Bang Liu	ACL2024	Embodied Instruction Following (EIF) is a crucial task in embodied learning, requiring agents to interact with their environment through egocentric observations to fulfill natural language instructions. Recent advancements have seen a surge in employing large language models (LLMs) within a framework-centric approach t...	long	https://arxiv.org/abs/2403.03017	https://aclanthology.org/2024.acl-long.37/	anthology
Multimodal Instruction Tuning with Conditional Mixture of LoRA	Ying Shen, Zhiyang Xu, Qifan Wang, Yu Cheng, Wenpeng Yin, Lifu Huang	ACL2024	Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in diverse tasks across different domains, with an increasing focus on improving their zero-shot generalization capabilities for unseen multimodal tasks. Multimodal instruction tuning has emerged as a successful strategy for achieving zer...	long	https://arxiv.org/abs/2402.15896	https://aclanthology.org/2024.acl-long.38/	anthology
DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation	Yiqing Xie, Sheng Zhang, Hao Cheng, Pengfei Liu, Zelalem Gero, Cliff Wong, Tristan Naumann, Hoifung Poon, Carolyn Rose	ACL2024	Medical text generation aims to assist with administrative work and highlight salient information to support decision-making.To reflect the specific requirements of medical text, in this paper, we propose a set of metrics to evaluate the completeness, conciseness, and attribution of the generated text at a fine-grained...	long	https://arxiv.org/abs/2311.09581	https://aclanthology.org/2024.acl-long.39/	anthology
FOFO: A Benchmark to Evaluate LLMs’ Format-Following Capability	Congying Xia, Chen Xing, Jiangshu Du, Xinyi Yang, Yihao Feng, Ran Xu, Wenpeng Yin, Caiming Xiong	ACL2024	This paper presents FoFo, a pioneering benchmark for evaluating large language models’ (LLMs) ability to follow complex, domain-specific formats, a crucial yet under-examined capability for their application as AI agents. Despite LLMs’ advancements, existing benchmarks fail to assess their format-following proficiency ...	long	https://arxiv.org/abs/2402.18667	https://aclanthology.org/2024.acl-long.40/	anthology
Hyper-CL: Conditioning Sentence Representations with Hypernetworks	Young Yoo, Jii Cha, Changhyeon Kim, Taeuk Kim	ACL2024	While the introduction of contrastive learning frameworks in sentence representation learning has significantly contributed to advancements in the field, it still remains unclear whether state-of-the-art sentence embeddings can capture the fine-grained semantics of sentences, particularly when conditioned on specific p...	long	https://arxiv.org/abs/2403.09490	https://aclanthology.org/2024.acl-long.41/	anthology
Analysis of Multi-Source Language Training in Cross-Lingual Transfer	Seonghoon Lim, Taejun Yun, Jinhyeon Kim, Jihun Choi, Taeuk Kim	ACL2024	The successful adaptation of multilingual language models (LMs) to a specific language-task pair critically depends on the availability of data tailored for that condition. While cross-lingual transfer (XLT) methods have contributed to addressing this data scarcity problem, there still exists ongoing debate about the m...	long	https://arxiv.org/abs/2402.13562	https://aclanthology.org/2024.acl-long.42/	anthology
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions	Sreyan Ghosh, Utkarsh Tyagi, Sonal Kumar, Chandra Kiran Evuru, Ramaneswaran S, S Sakshi, Dinesh Manocha	ACL2024	We present ABEX, a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks. ABEX is based on ABstract-and-EXpand, a novel paradigm for generating diverse forms of an input document – we first convert a document into its concise, abstract description and t...	long	https://arxiv.org/abs/2406.04286	https://aclanthology.org/2024.acl-long.43/	anthology
The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants	Lucas Bandarkar, Davis Liang, Benjamin Muller, Mikel Artetxe, Satya Narayan Shukla, Donald Husa, Naman Goyal, Abhinandan Krishnan, Luke Zettlemoyer, Madian Khabsa	ACL2024	We present Belebele, a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. Significantly expanding the language coverage of natural language understanding (NLU) benchmarks, this dataset enables the evaluation of text models in high-, medium-, and low-resource languages. Each ques...	long	https://arxiv.org/abs/2308.16884	https://aclanthology.org/2024.acl-long.44/	anthology
Learn from Failure: Fine-tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving	Chenyang An, Zhibo Chen, Qihao Ye, Emily First, Letian Peng, Jiayun Zhang, Zihan Wang, Sorin Lerner, Jingbo Shang	ACL2024	Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i.e. proof steps) to search through proof states. The current model, while trained solely on successful proof paths, faces a discrepancy at the inference stage, as it must sample and ...	long	https://arxiv.org/abs/2404.07382	https://aclanthology.org/2024.acl-long.45/	anthology
Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach	Saehyung Lee, Sangwon Yu, Junsung Park, Jihun Yi, Sungroh Yoon	ACL2024	In this paper, we primarily address the issue of dialogue-form context query within the interactive text-to-image retrieval task. Our methodology, PlugIR, actively utilizes the general instruction-following capability of LLMs in two ways. First, by reformulating the dialogue-form context, we eliminate the necessity of ...	long	https://arxiv.org/abs/2406.03411	https://aclanthology.org/2024.acl-long.46/	anthology
IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction	Inna Lin, Ashish Sharma, Christopher Rytting, Adam Miner, Jina Suh, Tim Althoff	ACL2024	Navigating certain communication situations can be challenging due to individuals’ lack of skills and the interference of strong emotions. However, effective learning opportunities are rarely accessible. In this work, we conduct a human-centered study that uses language models to simulate bespoke communication training...	long	https://arxiv.org/abs/2402.12556	https://aclanthology.org/2024.acl-long.47/	anthology
Token-wise Influential Training Data Retrieval for Large Language Models	Huawei Lin, Jikai Long, Zhaozhuo Xu, Weijie Zhao	ACL2024	Given a Large Language Model (LLM) generation, how can we identify which training data led to this generation? In this paper, we proposed RapidIn, a scalable framework adapting to LLMs for estimating the influence of each training data. The proposed framework consists of two stages: caching and retrieval. First, we com...	long	https://arxiv.org/abs/2405.11724	https://aclanthology.org/2024.acl-long.48/	anthology
VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks	Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Russ Salakhutdinov, Daniel Fried	ACL2024	Autonomous agents capable of planning, reasoning, and executing actions on the web offer a promising avenue for automating computer tasks. However, the majority of existing benchmarks primarily focus on text-based agents, neglecting many natural tasks that require visual information to effectively solve. Given that mos...	long	https://arxiv.org/abs/2401.13649	https://aclanthology.org/2024.acl-long.50/	anthology
FineSurE: Fine-grained Summarization Evaluation using LLMs	Hwanjun Song, Hang Su, Igor Shalyminov, Jason Cai, Saab Mansour	ACL2024	Automated evaluation is crucial for streamlining text summarization benchmarking and model development, given the costly and time-consuming nature of human evaluation. Traditional methods like ROUGE do not correlate well with human judgment, while recently proposed LLM-based metrics provide only summary-level assessmen...	long	https://arxiv.org/abs/2407.00908	https://aclanthology.org/2024.acl-long.51/	anthology
Tuning Large Multimodal Models for Videos using Reinforcement Learning from AI Feedback	Daechul Ahn, Yura Choi, Youngjae Yu, Dongyeop Kang, Jonghyun Choi	ACL2024	Recent advancements in large language models have influenced the development of video large multimodal models (VLMMs). Previous approaches for VLMMs involve Supervised Fine-Tuning (SFT) with instruction-tuned datasets, integrating LLM with visual encoders, and additional learnable parameters. Here, aligning video with ...	long	https://arxiv.org/abs/2402.03746	https://aclanthology.org/2024.acl-long.52/	anthology
Prompt Refinement with Image Pivot for Text-to-Image Generation	Jingtao Zhan, Qingyao Ai, Yiqun Liu, Yingwei Pan, Ting Yao, Jiaxin Mao, Shaoping Ma, Tao Mei	ACL2024	For text-to-image generation, automatically refining user-provided natural language prompts into the keyword-enriched prompts favored by systems is essential for the user experience. Such a prompt refinement process is analogous to translating the prompt from “user languages” into “system languages”. However, the scarc...	long	https://arxiv.org/abs/2407.00247	https://aclanthology.org/2024.acl-long.53/	anthology
Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation	Masato Mita, Soichiro Murakami, Akihiko Kato, Peinan Zhang	ACL2024	In response to the limitations of manual ad creation, significant research has been conducted in the field of automatic ad text generation (ATG). However, the lack of comprehensive benchmarks and well-defined problem sets has made comparing different methods challenging. To tackle these challenges, we standardize the t...	long	https://arxiv.org/abs/2309.12030	https://aclanthology.org/2024.acl-long.54/	anthology
AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation	Zhaowei Wang, Wei Fan, Qing Zong, Hongming Zhang, Sehyun Choi, Tianqing Fang, Xin Liu, Yangqiu Song, Ginny Wong, Simon See	ACL2024	Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study. Existing work shows that LLMs are deficient in abstract ability, and how to improve it remains unexplored. In this work, we design the framework AbsInstruct to enhance LLMs’ abstraction ability through instruction t...	long	https://arxiv.org/abs/2402.10646	https://aclanthology.org/2024.acl-long.55/	anthology
Reflect-RL: Two-Player Online RL Fine-Tuning for LMs	Runlong Zhou, Simon Du, Beibin Li	ACL2024	As language models (LMs) demonstrate their capabilities in various fields, their application to tasks requiring multi-round interactions has become increasingly popular. These tasks usually have complex dynamics, so supervised fine-tuning (SFT) on a limited offline dataset does not yield good performance. However, only...	long	https://arxiv.org/abs/2402.12621	https://aclanthology.org/2024.acl-long.56/	anthology
Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning	Zhaorui Yang, Tianyu Pang, Haozhe Feng, Han Wang, Wei Chen, Minfeng Zhu, Qian Liu	ACL2024	The surge in Large Language Models (LLMs) has revolutionized natural language processing, but fine-tuning them for specific tasks often encounters challenges in balancing performance and preserving general instruction-following abilities. In this paper, we posit that the distribution gap between task datasets and the L...	long	https://arxiv.org/abs/2402.13669	https://aclanthology.org/2024.acl-long.58/	anthology
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation	Kun Zhu, Xiaocheng Feng, Xiyuan Du, Yuxuan Gu, Weijiang Yu, Haotian Wang, Qianglong Chen, Zheng Chu, Jingchang Chen, Bing Qin	ACL2024	Retrieval-augmented generation integrates the capabilities of large language models with relevant information retrieved from an extensive corpus, yet encounters challenges when confronted with real-world noisy data. One recent solution is to train a filter module to find relevant content but only achieve suboptimal noi...	long	https://arxiv.org/abs/2406.01549	https://aclanthology.org/2024.acl-long.59/	anthology
RORA: Robust Free-Text Rationale Evaluation	Zhengping Jiang, Yining Lu, Hanjie Chen, Daniel Khashabi, Benjamin Van Durme, Anqi Liu	ACL2024	Free-text rationales play a pivotal role in explainable NLP, bridging the knowledge and reasoning gaps behind a model’s decision-making. However, due to the diversity of potential reasoning paths and a corresponding lack of definitive ground truth, their evaluation remains a challenge. Existing metrics rely on the degr...	long	https://arxiv.org/abs/2402.18678	https://aclanthology.org/2024.acl-long.60/	anthology
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents	Cheng Qian, Bingxiang He, Zhong Zhuang, Jia Deng, Yujia Qin, Xin Cong, Zhong Zhang, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun	ACL2024	Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. Although adept at devising strategies and performing tasks, these agents struggle with seeking clarification and grasping precise user intentions. To bri...	long	https://arxiv.org/abs/2402.09205	https://aclanthology.org/2024.acl-long.61/	anthology
ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models	Aparna Elangovan, Ling Liu, Lei Xu, Sravan Babu Bodapati, Dan Roth	ACL2024	In this position paper, we argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking that draws upon the insights from disciplines such as user experience research and human behavioral psychology to ensure that the experimental design and results are reliable. The ...	long	https://arxiv.org/abs/2405.18638	https://aclanthology.org/2024.acl-long.63/	anthology
Linguistically Conditioned Semantic Textual Similarity	Jingxuan Tu, Keer Xu, Liulu Yue, Bingyang Ye, Kyeongmin Rim, James Pustejovsky	ACL2024	Semantic textual similarity (STS) is a fundamental NLP task that measures the semantic similarity between a pair of sentences. In order to reduce the inherent ambiguity posed from the sentences, a recent work called Conditional STS (C-STS) has been proposed to measure the sentences’ similarity conditioned on a certain ...	long	https://arxiv.org/abs/2406.03673	https://aclanthology.org/2024.acl-long.64/	anthology
Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future	Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, Ting Liu	ACL2024	Reasoning, a fundamental cognitive process integral to human intelligence, has garnered substantial interest within artificial intelligence.Notably, recent studies have revealed that chain-of-thought prompting significantly enhances LLM’s reasoning capabilities, which attracts widespread attention from both academics a...	long	https://arxiv.org/abs/2309.15402	https://aclanthology.org/2024.acl-long.65/	anthology
TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models	Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Haotian Wang, Ming Liu, Bing Qin	ACL2024	Grasping the concept of time is a fundamental facet of human cognition, indispensable for truly comprehending the intricacies of the world.Previous studies typically focus on specific aspects of time, lacking a comprehensive temporal reasoning benchmark.To address this, we propose TimeBench, a comprehensive hierarchica...	long	https://arxiv.org/abs/2311.17667	https://aclanthology.org/2024.acl-long.66/	anthology
BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering	Zheng Chu, Jingchang Chen, Qianglong Chen, Haotian Wang, Kun Zhu, Xiyuan Du, Weijiang Yu, Ming Liu, Bing Qin	ACL2024	Large language models (LLMs) have demonstrated strong reasoning capabilities.Nevertheless, they still suffer from factual errors when tackling knowledge-intensive tasks.Retrieval-augmented reasoning represents a promising approach.However, significant challenges still persist, including inaccurate and insufficient retr...	long	https://arxiv.org/abs/2406.19820	https://aclanthology.org/2024.acl-long.67/	anthology
ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base	Siyu Yuan, Jiangjie Chen, Changzhi Sun, Jiaqing Liang, Yanghua Xiao, Deqing Yang	ACL2024	Analogical reasoning is a fundamental cognitive ability of humans. However, current language models (LMs) still struggle to achieve human-like performance in analogical reasoning tasks due to a lack of resources for model training. In this work, we address this gap by proposing ANALOGYKB, a million-scale analogy knowle...	long	https://arxiv.org/abs/2305.05994	https://aclanthology.org/2024.acl-long.68/	anthology
TaSL: Continual Dialog State Tracking via Task Skill Localization and Consolidation	Yujie Feng, Xu Chu, Yongxin Xu, Guangyuan Shi, Bo Liu, Xiao-Ming Wu	ACL2024	A practical dialogue system requires the capacity for ongoing skill acquisition and adaptability to new tasks while preserving prior knowledge. However, current methods for Continual Dialogue State Tracking (DST), a crucial function of dialogue systems, struggle with the catastrophic forgetting issue and knowledge tran...	long	https://arxiv.org/abs/2408.09857	https://aclanthology.org/2024.acl-long.69/	anthology
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models	Damai Dai, Chengqi Deng, Chenggang Zhao, R.x. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y. Wu, Zhenda Xie, Y.k. Li, Panpan Huang, Fuli Luo, Chong Ruan, Zhifang Sui, Wenfeng Liang	ACL2024	In the era of large language models, Mixture-of-Experts (MoE) is a promising architecture for managing computational costs when scaling up model parameters. However, conventional MoE architectures like GShard, which activate the top-K out of N experts, face challenges in ensuring expert specialization, i.e. each expert...	long	https://arxiv.org/abs/2401.06066	https://aclanthology.org/2024.acl-long.70/	anthology
Grounding Language Model with Chunking-Free In-Context Retrieval	Hongjin Qian, Zheng Liu, Kelong Mao, Yujia Zhou, Zhicheng Dou	ACL2024	This paper presents a novel Chunking-Free In-Context (CFIC) retrieval approach, specifically tailored for Retrieval-Augmented Generation (RAG) systems. Traditional RAG systems often struggle with grounding responses using precise evidence text due to the challenges of processing lengthy documents and filtering out irre...	long	https://arxiv.org/abs/2402.09760	https://aclanthology.org/2024.acl-long.71/	anthology
Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation	Jiaxin Bai, Yicheng Wang, Tianshi Zheng, Yue Guo, Xin Liu, Yangqiu Song	ACL2024	Abductive reasoning is the process of making educated guesses to provide explanations for observations. Although many applications require the use of knowledge for explanations, the utilization of abductive reasoning in conjunction with structured knowledge, such as a knowledge graph, remains largely unexplored. To fil...	long	https://arxiv.org/abs/2312.15643	https://aclanthology.org/2024.acl-long.72/	anthology
Active Prompting with Chain-of-Thought for Large Language Models	Shizhe Diao, Pengcheng Wang, Yong Lin, Rui Pan, Xiang Liu, Tong Zhang	ACL2024	The increasing scale of large language models (LLMs) brings emergent abilities to various complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is known that the effective design of task-specific prompts is critical for LLMs’ ability to produce high-quality answers. In particular, an effec...	long	https://arxiv.org/abs/2302.12246	https://aclanthology.org/2024.acl-long.73/	anthology
EasyGen: Easing Multimodal Generation with BiDiffuser and LLMs	Xiangyu Zhao, Bo Liu, Qijiong Liu, Guangyuan Shi, Xiao-Ming Wu	ACL2024	We present EasyGen, an efficient model designed to enhance multimodal understanding and generation by harnessing the capabilities of diffusion models and large language models (LLMs). Unlike existing multimodal models that predominately depend on encoders like CLIP or ImageBind and need ample amounts of training data t...	long	https://arxiv.org/abs/2310.08949	https://aclanthology.org/2024.acl-long.74/	anthology
Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search	Haochen Li, Xin Zhou, Zhiqi Shen	ACL2024	In code search, the Generation-Augmented Retrieval (GAR) framework, which generates exemplar code snippets to augment queries, has emerged as a promising strategy to address the principal challenge of modality misalignment between code snippets and natural language queries, particularly with the demonstrated code gener...	long	https://arxiv.org/abs/2401.04514	https://aclanthology.org/2024.acl-long.75/	anthology
A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications	Naomi Baes, Nick Haslam, Ekaterina Vylomova	ACL2024	Historical linguists have identified multiple forms of lexical semantic change. We present a three-dimensional framework for integrating these forms and a unified computational methodology for evaluating them concurrently. The dimensions represent increases or decreases in semantic 1) sentiment (valence of a target wor...	long	https://arxiv.org/abs/2406.06052	https://aclanthology.org/2024.acl-long.76/	anthology
Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal	Jianheng Huang, Leyang Cui, Ante Wang, Chengyi Yang, Xinting Liao, Linfeng Song, Junfeng Yao, Jinsong Su	ACL2024	Large language models (LLMs) suffer from catastrophic forgetting during continual learning. Conventional rehearsal-based methods rely on previous training data to retain the model’s ability, which may not be feasible in real-world applications. When conducting continual learning based on a publicly-released LLM checkpo...	long	https://arxiv.org/abs/2403.01244	https://aclanthology.org/2024.acl-long.77/	anthology
Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency	Baizhou Huang, Shuai Lu, Xiaojun Wan, Nan Duan	ACL2024	Large language models (LLMs) have exhibited remarkable ability in code generation. However, generating the correct solution in a single attempt still remains a challenge. Prior works utilize verification properties in software engineering to verify and re-rank solutions in a majority voting manner. But the assumption b...	long	https://arxiv.org/abs/2309.17272	https://aclanthology.org/2024.acl-long.78/	anthology
Citation-Enhanced Generation for LLM-based Chatbots	Weitao Li, Junkai Li, Weizhi Ma, Yang Liu	ACL2024	Large language models (LLMs) exhibit powerful general intelligence across diverse scenarios, including their integration into chatbots. However, a vital challenge of LLM-based chatbots is that they may produce hallucinated content in responses, which significantly limits their applicability. Various efforts have been m...	long	https://arxiv.org/abs/2402.16063	https://aclanthology.org/2024.acl-long.79/	anthology
Transitive Consistency Constrained Learning for Entity-to-Entity Stance Detection	Haoyang Wen, Eduard Hovy, Alexander Hauptmann	ACL2024	Entity-to-entity stance detection identifies the stance between a pair of entities with a directed link that indicates the source, target and polarity. It is a streamlined task without the complex dependency structure for structural sentiment analysis, while it is more informative compared to most previous work assumin...	long	https://arxiv.org/abs/2312.16054	https://aclanthology.org/2024.acl-long.80/	anthology
Feature-Adaptive and Data-Scalable In-Context Learning	Jiahao Li, Quan Wang, Licheng Zhang, Guoqing Jin, Zhendong Mao	ACL2024	In-context learning (ICL), which promotes inference with several demonstrations, has become a widespread paradigm to stimulate LLM capabilities for downstream tasks. Due to context length constraints, it cannot be further improved in spite of more training data, and general features directly from LLMs in ICL are not ad...	long	https://arxiv.org/abs/2405.10738	https://aclanthology.org/2024.acl-long.81/	anthology
Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games	Yizhe Zhang, Jiarui Lu, Navdeep Jaitly	ACL2024	Large language models (LLMs) are effective at answering questions that are clearly asked. However, when faced with ambiguous queries they can act unpredictably and produce incorrect outputs. This underscores the need for the development of intelligent agents capable of asking clarification questions to resolve ambiguit...	long	https://arxiv.org/abs/2310.01468	https://aclanthology.org/2024.acl-long.82/	anthology
WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models	Shangqing Tu, Yuliang Sun, Yushi Bai, Jifan Yu, Lei Hou, Juanzi Li	ACL2024	To mitigate the potential misuse of large language models (LLMs), recent research has developed watermarking algorithms, which restrict the generation process to leave an invisible trace for watermark detection. Due to the two-stage nature of the task, most studies evaluate the generation and detection separately, ther...	long	https://arxiv.org/abs/2311.07138	https://aclanthology.org/2024.acl-long.83/	anthology
Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models	Yida Zhao, Chao Lou, Kewei Tu	ACL2024	Syntactic Transformer language models aim to achieve better generalization through simultaneously modeling syntax trees and sentences. While prior work has been focusing on adding constituency-based structures to Transformers, we introduce Dependency Transformer Grammars (DTGs), a new class of Transformer language mode...	long	https://arxiv.org/abs/2407.17406	https://aclanthology.org/2024.acl-long.84/	anthology
A Non-autoregressive Generation Framework for End-to-End Simultaneous Speech-to-Any Translation	Zhengrui Ma, Qingkai Fang, Shaolei Zhang, Shoutao Guo, Yang Feng, Min Zhang	ACL2024	Simultaneous translation models play a crucial role in facilitating communication. However, existing research primarily focuses on text-to-text or speech-to-text models, necessitating additional cascade components to achieve speech-to-speech translation. These pipeline methods suffer from error propagation and accumula...	long	https://arxiv.org/abs/2406.06937	https://aclanthology.org/2024.acl-long.85/	anthology
Probing Language Models for Pre-training Data Detection	Zhenhua Liu, Tong Zhu, Chuanyuan Tan, Bing Liu, Haonan Lu, Wenliang Chen	ACL2024	Large Language Models (LLMs) have shown their impressive capabilities, while also raising concerns about the data contamination problems due to privacy issues and leakage of benchmark datasets in the pre-training phase. Therefore, it is vital to detect the contamination by checking whether an LLM has been pre-trained o...	long	https://arxiv.org/abs/2406.01333	https://aclanthology.org/2024.acl-long.86/	anthology
Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding	Zhihan Zhang, Yixin Cao, Chenchen Ye, Yunshan Ma, Lizi Liao, Tat-Seng Chua	ACL2024	The digital landscape is rapidly evolving with an ever-increasing volume of online news, emphasizing the need for swift and precise analysis of complex events.We refer to the complex events composed of many news articles over an extended period as Temporal Complex Event (TCE). This paper proposes a novel approach using...	long	https://arxiv.org/abs/2406.02472	https://aclanthology.org/2024.acl-long.87/	anthology
IBSEN: Director-Actor Agent Collaboration for Controllable and Interactive Drama Script Generation	Senyu Han, Lu Chen, Li-Min Lin, Zhengshan Xu, Kai Yu	ACL2024	Large language models have demonstrated their capabilities in storyline creation and human-like character role-playing. Current language model agents mainly focus on reasonable behaviors from the level of individuals, and their behaviors might be hard to constraint on the level of the whole storyline. In this paper we ...	long	https://arxiv.org/abs/2407.01093	https://aclanthology.org/2024.acl-long.88/	anthology
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression	Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, Lili Qiu	ACL2024	In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua...	long	https://arxiv.org/abs/2310.06839	https://aclanthology.org/2024.acl-long.91/	anthology
HealMe: Harnessing Cognitive Reframing in Large Language Models for Psychotherapy	Mengxi Xiao, Qianqian Xie, Ziyan Kuang, Zhicheng Liu, Kailai Yang, Min Peng, Weiguang Han, Jimin Huang	ACL2024	Large Language Models (LLMs) can play a vital role in psychotherapy by adeptly handling the crucial task of cognitive reframing and overcoming challenges such as shame, distrust, therapist skill variability, and resource scarcity. Previous LLMs in cognitive reframing mainly converted negative emotions to positive ones,...	long	https://arxiv.org/abs/2403.05574	https://aclanthology.org/2024.acl-long.93/	anthology
Multimodal Prompt Learning with Missing Modalities for Sentiment Analysis and Emotion Recognition	Zirun Guo, Tao Jin, Zhou Zhao	ACL2024	The development of multimodal models has significantly advanced multimodal sentiment analysis and emotion recognition. However, in real-world applications, the presence of various missing modality cases often leads to a degradation in the model’s performance. In this work, we propose a novel multimodal Transformer fram...	long	https://arxiv.org/abs/2407.05374	https://aclanthology.org/2024.acl-long.94/	anthology
An Effective Pronunciation Assessment Approach Leveraging Hierarchical Transformers and Pre-training Strategies	Bi-Cheng Yan, Jiun-Ting Li, Yi-Cheng Wang, Hsin Wei Wang, Tien-Hong Lo, Yung-Chang Hsu, Wei-Cheng Chao, Berlin Chen	ACL2024	Automatic pronunciation assessment (APA) manages to quantify a second language (L2) learner’s pronunciation proficiency in a target language by providing fine-grained feedback with multiple pronunciation aspect scores at various linguistic levels. Most existing efforts on APA typically parallelize the modeling process,...	long	https://arxiv.org/abs/2512.04964	https://aclanthology.org/2024.acl-long.95/	anthology
Detection-Correction Structure via General Language Model for Grammatical Error Correction	Wei Li, Houfeng Wang	ACL2024	Grammatical error correction (GEC) is a task dedicated to rectifying texts with minimal edits, which can be decoupled into two components: detection and correction. However, previous works have predominantly focused on direct correction, with no prior efforts to integrate both into a single model. Moreover, the explora...	long	https://arxiv.org/abs/2405.17804	https://aclanthology.org/2024.acl-long.96/	anthology
Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer	Yongxin Zhu, Dan Su, Liqiang He, Linli Xu, Dong Yu	ACL2024	While recent advancements in speech language models have achieved significant progress, they face remarkable challenges in modeling the long acoustic sequences of neural audio codecs. In this paper, we introduce Generative Pre-trained Speech Transformer (GPST), a hierarchical transformer designed for efficient speech l...	long	https://arxiv.org/abs/2406.00976	https://aclanthology.org/2024.acl-long.97/	anthology
Selene: Pioneering Automated Proof in Software Verification	Lichen Zhang, Shuai Lu, Nan Duan	ACL2024	Ensuring correctness is a pivotal aspect of software engineering. Among the various strategies available, software verification offers a definitive assurance of correctness. Nevertheless, writing verification proofs is resource-intensive and manpower-consuming, and there is a great need to automate this process. We int...	long	https://arxiv.org/abs/2401.07663	https://aclanthology.org/2024.acl-long.98/	anthology
Dissecting Human and LLM Preferences	Junlong Li, Fan Zhou, Shichao Sun, Yikai Zhang, Hai Zhao, Pengfei Liu	ACL2024	As a relative quality comparison of model responses, human and Large Language Model (LLM) preferences serve as common alignment goals in model fine-tuning and criteria in evaluation. Yet, these preferences merely reflect broad tendencies, resulting in less explainable and controllable models with potential safety risks...	long	https://arxiv.org/abs/2402.11296	https://aclanthology.org/2024.acl-long.99/	anthology
UniCoder: Scaling Code Large Language Model via Universal Code	Tao Sun, Linzheng Chai, Jian Yang, Yuwei Yin, Hongcheng Guo, Jiaheng Liu, Bing Wang, Liqun Yang, Zhoujun Li	ACL2024	Intermediate reasoning or acting steps have successfully improved large language models (LLMs) for handling various downstream natural language processing (NLP) tasks.When applying LLMs for code generation, recent works mainly focus on directing the models to articulate intermediate natural-language reasoning steps, as...	long	https://arxiv.org/abs/2406.16441	https://aclanthology.org/2024.acl-long.100/	anthology
AoE: Angle-optimized Embeddings for Semantic Textual Similarity	Xianming Li, Jing Li	ACL2024	Text embedding is pivotal in semantic textual similarity (STS) tasks, which are crucial components in Large Language Model (LLM) applications. STS learning largely relies on the cosine function as the optimization objective to reflect semantic similarity. However, the cosine has saturation zones rendering vanishing gra...	long	https://arxiv.org/abs/2309.12871	https://aclanthology.org/2024.acl-long.101/	anthology
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews	Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao	ACL2024	Role-playing agents (RPAs), powered by large language models, have emerged as a flourishing field of applications. However, a key challenge lies in assessing whether RPAs accurately reproduce the personas of target characters, namely their character fidelity. Existing methods mainly focus on the knowledge and linguisti...	long	https://arxiv.org/abs/2310.17976	https://aclanthology.org/2024.acl-long.102/	anthology
Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better	Shengchao Liu, Xiaoming Liu, Yichen Wang, Zehua Cheng, Chengzhengxu Li, Zhaohan Zhang, Yu Lan, Chao Shen	ACL2024	The burgeoning generative capabilities of large language models (LLMs) have raised growing concerns about abuse, demanding automatic machine-generated text detectors. DetectGPT, a zero-shot metric-based detector, first introduces perturbation and shows great performance improvement. However, in DetectGPT, the random pe...	long	https://arxiv.org/abs/2402.00263	https://aclanthology.org/2024.acl-long.103/	anthology
AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators	Jingwei Ni, Minjing Shi, Dominik Stammbach, Mrinmaya Sachan, Elliott Ash, Markus Leippold	ACL2024	With the rise of generative AI, automated fact-checking methods to combat misinformation are becoming more and more important. However, factual claim detection, the first step in a fact-checking pipeline, suffers from two key issues that limit its scalability and generalizability: (1) inconsistency in definitions of th...	long	https://arxiv.org/abs/2402.11073	https://aclanthology.org/2024.acl-long.104/	anthology
Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering	Tobias Schimanski, Jingwei Ni, Mathias Kraus, Elliott Ash, Markus Leippold	ACL2024	Advances towards more faithful and traceable answers of Large Language Models (LLMs) are crucial for various research and practical endeavors. One avenue in reaching this goal is basing the answers on reliable sources. However, this Evidence-Based QA has proven to work insufficiently with LLMs in terms of citing the co...	long	https://arxiv.org/abs/2402.08277	https://aclanthology.org/2024.acl-long.105/	anthology
LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin	Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Wei Shen, Limao Xiong, Yuhao Zhou, Xiao Wang, Zhiheng Xi, Xiaoran Fan, Shiliang Pu, Jiang Zhu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang	ACL2024	Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhance their capabilities in downstream tasks. Substantially increasing instruction data is a direct solution to align the model with a broader range of downstream tasks or notably improv...	long	https://arxiv.org/abs/2312.09979	https://aclanthology.org/2024.acl-long.106/	anthology
Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation	Xiaoying Zhang, Baolin Peng, Ye Tian, Jingyan Zhou, Lifeng Jin, Linfeng Song, Haitao Mi, Helen Meng	ACL2024	Despite showing impressive abilities, large language models (LLMs) often struggle with factual inaccuracies, i.e., ”hallucinations”, even when they hold relevant knowledge. To mitigate these hallucinations, current approaches typically necessitate high-quality human factuality annotations. In this work, we explore Self...	long	https://arxiv.org/abs/2402.09267	https://aclanthology.org/2024.acl-long.107/	anthology
M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions	Zheng Wang, Shu Teo, Jieer Ouyang, Yongjun Xu, Wei Shi	ACL2024	Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant memories from an external database. However, existing RAG methods typically organize all memories in a whole database, potentially limiting focus on crucial memories and introducing noise. In this paper, we introduce a mul...	long	https://arxiv.org/abs/2405.16420	https://aclanthology.org/2024.acl-long.108/	anthology

End of preview. Expand in Data Studio

Top Conference Papers (2024-2026)

Paper metadata from 10 top computer-science conferences (ML / NLP / IR / Data Mining / RecSys), covering 2024-2026. 38,201 papers in total.

Structure

config = conference: ACL, CIKM, ICLR, ICML, KDD, NeurIPS, RecSys, SIGIR, WSDM, WWW
split = year: 2024, 2025, and 2026 where available

from datasets import load_dataset
ds = load_dataset("yufan/top-conference-papers", "ICLR")   # one conference
ds["2024"]                                                 # one year

Fields (8, all string / nullable)

field	description
`title`	paper title
`authors`	comma-separated author names
`venue_short`	conference + year, e.g. `ICLR2024`
`abstract`	abstract - present for OpenReview/Anthology sources; `null` for DBLP
`decision`	acceptance type (e.g. `poster`, `oral`, `long`) - `null` for DBLP
`arxiv_url`	arXiv abstract URL (~76% populated)
`source_url`	official page (ACL Anthology / OpenReview / DBLP)
`source_type`	one of `anthology`, `openreview`, `dblp`

Sources

OpenReview / ACL Anthology (ICLR, ICML, NeurIPS, ACL): include abstract and decision.
DBLP (CIKM, KDD, RecSys, SIGIR, WSDM, WWW): bibliographic records; arxiv_url matched separately.

2026 editions are available for ACL, ICLR, WSDM, WWW (partial, reflecting accepted lists released so far).

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