ACL
Collection
Accepted papers for ACL (Annual Meeting of the Association for Computational Linguistics), one dataset per year. • 14 items • Updated
paper_id stringlengths 15 17 | title stringlengths 21 144 | paper_url stringlengths 41 43 | authors listlengths 1 43 | abstract large_stringlengths 470 1.87k ⌀ | anthology_id stringlengths 15 17 | doi stringlengths 27 29 | award stringclasses 16
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|
2024.acl-long.1 | Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models | https://aclanthology.org/2024.acl-long.1/ | [
"Zhengxin Zhang",
"Dan Zhao",
"Xupeng Miao",
"Gabriele Oliaro",
"Zhihao Zhang",
"Qing Li",
"Yong Jiang",
"Zhihao Jia"
] | 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... | 2024.acl-long.1 | 10.18653/v1/2024.acl-long.1 | Outstanding Paper Award | 2401.07159 | title_snapshot |
2024.acl-long.2 | Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances | https://aclanthology.org/2024.acl-long.2/ | [
"Hanlei Zhang",
"Hua Xu",
"Fei Long",
"Xin Wang",
"Kai Gao"
] | 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... | 2024.acl-long.2 | 10.18653/v1/2024.acl-long.2 | null | 2405.12775 | title_snapshot |
2024.acl-long.3 | MAGE: Machine-generated Text Detection in the Wild | https://aclanthology.org/2024.acl-long.3/ | [
"Yafu Li",
"Qintong Li",
"Leyang Cui",
"Wei Bi",
"Zhilin Wang",
"Longyue Wang",
"Linyi Yang",
"Shuming Shi",
"Yue Zhang"
] | 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... | 2024.acl-long.3 | 10.18653/v1/2024.acl-long.3 | null | 2305.13242 | title_snapshot |
2024.acl-long.4 | PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models | https://aclanthology.org/2024.acl-long.4/ | [
"Haoran Li",
"Dadi Guo",
"Donghao Li",
"Wei Fan",
"Qi Hu",
"Xin Liu",
"Chunkit Chan",
"Duanyi Yao",
"Yuan Yao",
"Yangqiu Song"
] | 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... | 2024.acl-long.4 | 10.18653/v1/2024.acl-long.4 | null | 2311.04044 | title_snapshot |
2024.acl-long.5 | GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators | https://aclanthology.org/2024.acl-long.5/ | [
"Yuchen Hu",
"Chen Chen",
"Chao-Han Huck Yang",
"Ruizhe Li",
"Dong Zhang",
"Zhehuai Chen",
"Eng Siong Chng"
] | 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... | 2024.acl-long.5 | 10.18653/v1/2024.acl-long.5 | null | 2402.06894 | title_snapshot |
2024.acl-long.6 | Exploring Chain-of-Thought for Multi-modal Metaphor Detection | https://aclanthology.org/2024.acl-long.6/ | [
"Yanzhi Xu",
"Yueying Hua",
"Shichen Li",
"Zhongqing Wang"
] | Metaphors are commonly found in advertising and internet memes. However, the free form of internet memes often leads to a lack of high-quality textual data. Metaphor detection demands a deep interpretation of both textual and visual elements, requiring extensive common-sense knowledge, which poses a challenge to langua... | 2024.acl-long.6 | 10.18653/v1/2024.acl-long.6 | null | null | null |
2024.acl-long.7 | BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation | https://aclanthology.org/2024.acl-long.7/ | [
"DaYou Du",
"Yijia Zhang",
"Shijie Cao",
"Jiaqi Guo",
"Ting Cao",
"Xiaowen Chu",
"Ningyi Xu"
] | 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... | 2024.acl-long.7 | 10.18653/v1/2024.acl-long.7 | null | 2402.10631 | title_snapshot |
2024.acl-long.8 | A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation | https://aclanthology.org/2024.acl-long.8/ | [
"Kai Chen",
"Ye Wang",
"Yitong Li",
"Aiping Li",
"Han Yu",
"Xin Song"
] | 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... | 2024.acl-long.8 | 10.18653/v1/2024.acl-long.8 | null | 2405.18106 | title_snapshot |
2024.acl-long.9 | Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation | https://aclanthology.org/2024.acl-long.9/ | [
"Shicheng Xu",
"Liang Pang",
"Mo Yu",
"Fandong Meng",
"Huawei Shen",
"Xueqi Cheng",
"Jie Zhou"
] | 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... | 2024.acl-long.9 | 10.18653/v1/2024.acl-long.9 | null | 2402.18150 | title_snapshot |
2024.acl-long.10 | CSCD-NS: a Chinese Spelling Check Dataset for Native Speakers | https://aclanthology.org/2024.acl-long.10/ | [
"Yong Hu",
"Fandong Meng",
"Jie Zhou"
] | 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... | 2024.acl-long.10 | 10.18653/v1/2024.acl-long.10 | null | 2211.08788 | title_snapshot |
2024.acl-long.11 | Evaluating Dynamic Topic Models | https://aclanthology.org/2024.acl-long.11/ | [
"Charu Karakkaparambil James",
"Mayank Nagda",
"Nooshin Haji Ghassemi",
"Marius Kloft",
"Sophie Fellenz"
] | 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... | 2024.acl-long.11 | 10.18653/v1/2024.acl-long.11 | null | 2309.08627 | title_snapshot |
2024.acl-long.12 | How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition | https://aclanthology.org/2024.acl-long.12/ | [
"Guanting Dong",
"Hongyi Yuan",
"Keming Lu",
"Chengpeng Li",
"Mingfeng Xue",
"Dayiheng Liu",
"Wei Wang",
"Zheng Yuan",
"Chang Zhou",
"Jingren Zhou"
] | 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... | 2024.acl-long.12 | 10.18653/v1/2024.acl-long.12 | null | 2310.05492 | title_snapshot |
2024.acl-long.13 | Through the Lens of Split Vote: Exploring Disagreement, Difficulty and Calibration in Legal Case Outcome Classification | https://aclanthology.org/2024.acl-long.13/ | [
"Shanshan Xu",
"Santosh T.y.s.s",
"Oana Ichim",
"Barbara Plank",
"Matthias Grabmair"
] | 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... | 2024.acl-long.13 | 10.18653/v1/2024.acl-long.13 | null | 2402.07214 | title_snapshot |
2024.acl-long.14 | Inference to the Best Explanation in Large Language Models | https://aclanthology.org/2024.acl-long.14/ | [
"Dhairya Dalal",
"Marco Valentino",
"Andre Freitas",
"Paul Buitelaar"
] | 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’... | 2024.acl-long.14 | 10.18653/v1/2024.acl-long.14 | null | 2402.10767 | title_snapshot |
2024.acl-long.15 | A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus | https://aclanthology.org/2024.acl-long.15/ | [
"Eduard Poesina",
"Cornelia Caragea",
"Radu Tudor Ionescu"
] | 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... | 2024.acl-long.15 | 10.18653/v1/2024.acl-long.15 | null | 2405.11877 | title_snapshot |
2024.acl-long.16 | MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering | https://aclanthology.org/2024.acl-long.16/ | [
"Xiusi Chen",
"Jyun-Yu Jiang",
"Wei-Cheng Chang",
"Cho-Jui Hsieh",
"Hsiang-Fu Yu",
"Wei Wang"
] | 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 ... | 2024.acl-long.16 | 10.18653/v1/2024.acl-long.16 | null | 2310.05007 | title_snapshot |
2024.acl-long.17 | SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs | https://aclanthology.org/2024.acl-long.17/ | [
"Yebowen Hu",
"Kaiqiang Song",
"Sangwoo Cho",
"Xiaoyang Wang",
"Hassan Foroosh",
"Dong Yu",
"Fei Liu"
] | 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 ... | 2024.acl-long.17 | 10.18653/v1/2024.acl-long.17 | null | 2402.10979 | title_snapshot |
2024.acl-long.18 | SciMON: Scientific Inspiration Machines Optimized for Novelty | https://aclanthology.org/2024.acl-long.18/ | [
"Qingyun Wang",
"Doug Downey",
"Heng Ji",
"Tom Hope"
] | 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... | 2024.acl-long.18 | 10.18653/v1/2024.acl-long.18 | null | 2305.14259 | title_snapshot |
2024.acl-long.19 | Expedited Training of Visual Conditioned Language Generation via Redundancy Reduction | https://aclanthology.org/2024.acl-long.19/ | [
"Yiren Jian",
"Tingkai Liu",
"Yunzhe Tao",
"Chunhui Zhang",
"Soroush Vosoughi",
"Hongxia Yang"
] | 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 ... | 2024.acl-long.19 | 10.18653/v1/2024.acl-long.19 | null | 2310.03291 | title_snapshot |
2024.acl-long.20 | Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models | https://aclanthology.org/2024.acl-long.20/ | [
"Abhishek Kumar",
"Robert Morabito",
"Sanzhar Umbet",
"Jad Kabbara",
"Ali Emami"
] | 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... | 2024.acl-long.20 | 10.18653/v1/2024.acl-long.20 | null | 2405.16282 | title_snapshot |
2024.acl-long.21 | Retrieval-Augmented Multilingual Knowledge Editing | https://aclanthology.org/2024.acl-long.21/ | [
"Weixuan Wang",
"Barry Haddow",
"Alexandra Birch"
] | 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... | 2024.acl-long.21 | 10.18653/v1/2024.acl-long.21 | null | 2312.13040 | title_snapshot |
2024.acl-long.22 | Picturing Ambiguity: A Visual Twist on the Winograd Schema Challenge | https://aclanthology.org/2024.acl-long.22/ | [
"Brendan Park",
"Madeline Janecek",
"Naser Ezzati-Jivan",
"Yifeng Li",
"Ali Emami"
] | 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 ... | 2024.acl-long.22 | 10.18653/v1/2024.acl-long.22 | null | 2405.16277 | title_snapshot |
2024.acl-long.23 | Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models | https://aclanthology.org/2024.acl-long.23/ | [
"Abhishek Kumar",
"Sarfaroz Yunusov",
"Ali Emami"
] | 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... | 2024.acl-long.23 | 10.18653/v1/2024.acl-long.23 | null | 2405.14555 | title_snapshot |
2024.acl-long.24 | Framing in the Presence of Supporting Data: A Case Study in U.S. Economic News | https://aclanthology.org/2024.acl-long.24/ | [
"Alexandria Leto",
"Elliot Pickens",
"Coen Needell",
"David Rothschild",
"Maria Leonor Pacheco"
] | 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... | 2024.acl-long.24 | 10.18653/v1/2024.acl-long.24 | null | 2402.14224 | title_snapshot |
2024.acl-long.25 | Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences | https://aclanthology.org/2024.acl-long.25/ | [
"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"
] | 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, ... | 2024.acl-long.25 | 10.18653/v1/2024.acl-long.25 | null | 2401.10529 | title_snapshot |
2024.acl-long.26 | TTM-RE: Memory-Augmented Document-Level Relation Extraction | https://aclanthology.org/2024.acl-long.26/ | [
"Chufan Gao",
"Xuan Wang",
"Jimeng Sun"
] | 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 ... | 2024.acl-long.26 | 10.18653/v1/2024.acl-long.26 | null | 2406.05906 | title_snapshot |
2024.acl-long.27 | Answer is All You Need: Instruction-following Text Embedding via Answering the Question | https://aclanthology.org/2024.acl-long.27/ | [
"Letian Peng",
"Yuwei Zhang",
"Zilong Wang",
"Jayanth Srinivasa",
"Gaowen Liu",
"Zihan Wang",
"Jingbo Shang"
] | 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... | 2024.acl-long.27 | 10.18653/v1/2024.acl-long.27 | null | 2402.09642 | title_snapshot |
2024.acl-long.28 | Explore Spurious Correlations at the Concept Level in Language Models for Text Classification | https://aclanthology.org/2024.acl-long.28/ | [
"Yuhang Zhou",
"Paiheng Xu",
"Xiaoyu Liu",
"Bang An",
"Wei Ai",
"Furong Huang"
] | 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... | 2024.acl-long.28 | 10.18653/v1/2024.acl-long.28 | null | 2311.08648 | title_snapshot |
2024.acl-long.29 | Every Answer Matters: Evaluating Commonsense with Probabilistic Measures | https://aclanthology.org/2024.acl-long.29/ | [
"Qi Cheng",
"Michael Boratko",
"Pranay Kumar Yelugam",
"Tim O’Gorman",
"Nalini Singh",
"Andrew McCallum",
"Xiang Lorraine Li"
] | 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... | 2024.acl-long.29 | 10.18653/v1/2024.acl-long.29 | null | 2406.04145 | title_snapshot |
2024.acl-long.30 | GradSafe: Detecting Jailbreak Prompts for LLMs via Safety-Critical Gradient Analysis | https://aclanthology.org/2024.acl-long.30/ | [
"Yueqi Xie",
"Minghong Fang",
"Renjie Pi",
"Neil Gong"
] | 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,... | 2024.acl-long.30 | 10.18653/v1/2024.acl-long.30 | null | 2402.13494 | title_snapshot |
2024.acl-long.31 | Pouring Your Heart Out: Investigating the Role of Figurative Language in Online Expressions of Empathy | https://aclanthology.org/2024.acl-long.31/ | [
"Gyeongeun Lee",
"Christina Wong",
"Meghan Guo",
"Natalie Parde"
] | Empathy is a social mechanism used to support and strengthen emotional connection with others, including in online communities. However, little is currently known about the nature of these online expressions, nor the particular factors that may lead to their improved detection. In this work, we study the role of a spec... | 2024.acl-long.31 | 10.18653/v1/2024.acl-long.31 | null | null | null |
2024.acl-long.32 | An Information-Theoretic Approach to Analyze NLP Classification Tasks | https://aclanthology.org/2024.acl-long.32/ | [
"Luran Wang",
"Mark Gales",
"Vatsal Raina"
] | 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... | 2024.acl-long.32 | 10.18653/v1/2024.acl-long.32 | null | 2402.00978 | title_snapshot |
2024.acl-long.33 | Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders | https://aclanthology.org/2024.acl-long.33/ | [
"Yuwei Zhang",
"Siffi Singh",
"Sailik Sengupta",
"Igor Shalyminov",
"Hang Su",
"Hwanjun Song",
"Saab Mansour"
] | 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... | 2024.acl-long.33 | 10.18653/v1/2024.acl-long.33 | null | 2403.04314 | title_snapshot |
2024.acl-long.34 | Wav2Gloss: Generating Interlinear Glossed Text from Speech | https://aclanthology.org/2024.acl-long.34/ | [
"Taiqi He",
"Kwanghee Choi",
"Lindia Tjuatja",
"Nathaniel Robinson",
"Jiatong Shi",
"Shinji Watanabe",
"Graham Neubig",
"David Mortensen",
"Lori Levin"
] | 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... | 2024.acl-long.34 | 10.18653/v1/2024.acl-long.34 | null | 2403.13169 | title_snapshot |
2024.acl-long.35 | Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification | https://aclanthology.org/2024.acl-long.35/ | [
"Yibo Hu",
"Erick Skorupa Parolin",
"Latifur Khan",
"Patrick Brandt",
"Javier Osorio",
"Vito D’Orazio"
] | 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... | 2024.acl-long.35 | 10.18653/v1/2024.acl-long.35 | null | 2308.07876 | title_snapshot |
2024.acl-long.36 | SPOR: A Comprehensive and Practical Evaluation Method for Compositional Generalization in Data-to-Text Generation | https://aclanthology.org/2024.acl-long.36/ | [
"Ziyao Xu",
"Houfeng Wang"
] | 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... | 2024.acl-long.36 | 10.18653/v1/2024.acl-long.36 | null | 2405.10650 | title_snapshot |
2024.acl-long.37 | OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following | https://aclanthology.org/2024.acl-long.37/ | [
"Haochen Shi",
"Zhiyuan Sun",
"Xingdi Yuan",
"Marc-Alexandre Côté",
"Bang Liu"
] | 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... | 2024.acl-long.37 | 10.18653/v1/2024.acl-long.37 | null | 2403.03017 | title_snapshot |
2024.acl-long.38 | Multimodal Instruction Tuning with Conditional Mixture of LoRA | https://aclanthology.org/2024.acl-long.38/ | [
"Ying Shen",
"Zhiyang Xu",
"Qifan Wang",
"Yu Cheng",
"Wenpeng Yin",
"Lifu Huang"
] | 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... | 2024.acl-long.38 | 10.18653/v1/2024.acl-long.38 | null | 2402.15896 | title_snapshot |
2024.acl-long.39 | DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation | https://aclanthology.org/2024.acl-long.39/ | [
"Yiqing Xie",
"Sheng Zhang",
"Hao Cheng",
"Pengfei Liu",
"Zelalem Gero",
"Cliff Wong",
"Tristan Naumann",
"Hoifung Poon",
"Carolyn Rose"
] | 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... | 2024.acl-long.39 | 10.18653/v1/2024.acl-long.39 | null | 2311.09581 | title_snapshot |
2024.acl-long.40 | FOFO: A Benchmark to Evaluate LLMs’ Format-Following Capability | https://aclanthology.org/2024.acl-long.40/ | [
"Congying Xia",
"Chen Xing",
"Jiangshu Du",
"Xinyi Yang",
"Yihao Feng",
"Ran Xu",
"Wenpeng Yin",
"Caiming Xiong"
] | 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 ... | 2024.acl-long.40 | 10.18653/v1/2024.acl-long.40 | null | 2402.18667 | title_snapshot |
2024.acl-long.41 | Hyper-CL: Conditioning Sentence Representations with Hypernetworks | https://aclanthology.org/2024.acl-long.41/ | [
"Young Yoo",
"Jii Cha",
"Changhyeon Kim",
"Taeuk Kim"
] | 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... | 2024.acl-long.41 | 10.18653/v1/2024.acl-long.41 | null | 2403.09490 | title_snapshot |
2024.acl-long.42 | Analysis of Multi-Source Language Training in Cross-Lingual Transfer | https://aclanthology.org/2024.acl-long.42/ | [
"Seonghoon Lim",
"Taejun Yun",
"Jinhyeon Kim",
"Jihun Choi",
"Taeuk Kim"
] | 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... | 2024.acl-long.42 | 10.18653/v1/2024.acl-long.42 | null | 2402.13562 | title_snapshot |
2024.acl-long.43 | ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions | https://aclanthology.org/2024.acl-long.43/ | [
"Sreyan Ghosh",
"Utkarsh Tyagi",
"Sonal Kumar",
"Chandra Kiran Evuru",
"Ramaneswaran S",
"S Sakshi",
"Dinesh Manocha"
] | 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... | 2024.acl-long.43 | 10.18653/v1/2024.acl-long.43 | null | 2406.04286 | title_snapshot |
2024.acl-long.44 | The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants | https://aclanthology.org/2024.acl-long.44/ | [
"Lucas Bandarkar",
"Davis Liang",
"Benjamin Muller",
"Mikel Artetxe",
"Satya Narayan Shukla",
"Donald Husa",
"Naman Goyal",
"Abhinandan Krishnan",
"Luke Zettlemoyer",
"Madian Khabsa"
] | 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... | 2024.acl-long.44 | 10.18653/v1/2024.acl-long.44 | null | 2308.16884 | title_snapshot |
2024.acl-long.45 | Learn from Failure: Fine-tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving | https://aclanthology.org/2024.acl-long.45/ | [
"Chenyang An",
"Zhibo Chen",
"Qihao Ye",
"Emily First",
"Letian Peng",
"Jiayun Zhang",
"Zihan Wang",
"Sorin Lerner",
"Jingbo Shang"
] | 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 ... | 2024.acl-long.45 | 10.18653/v1/2024.acl-long.45 | null | 2404.07382 | title_snapshot |
2024.acl-long.46 | Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach | https://aclanthology.org/2024.acl-long.46/ | [
"Saehyung Lee",
"Sangwon Yu",
"Junsung Park",
"Jihun Yi",
"Sungroh Yoon"
] | 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 ... | 2024.acl-long.46 | 10.18653/v1/2024.acl-long.46 | null | 2406.03411 | title_snapshot |
2024.acl-long.47 | IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction | https://aclanthology.org/2024.acl-long.47/ | [
"Inna Lin",
"Ashish Sharma",
"Christopher Rytting",
"Adam Miner",
"Jina Suh",
"Tim Althoff"
] | 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... | 2024.acl-long.47 | 10.18653/v1/2024.acl-long.47 | null | 2402.12556 | title_snapshot |
2024.acl-long.48 | Token-wise Influential Training Data Retrieval for Large Language Models | https://aclanthology.org/2024.acl-long.48/ | [
"Huawei Lin",
"Jikai Long",
"Zhaozhuo Xu",
"Weijie Zhao"
] | 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... | 2024.acl-long.48 | 10.18653/v1/2024.acl-long.48 | null | 2405.11724 | title_snapshot |
2024.acl-long.49 | Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection | https://aclanthology.org/2024.acl-long.49/ | [
"Maxwell Weinzierl",
"Sanda Harabagiu"
] | Stance detection enables the inference of attitudes from human communications. Automatic stance identification was mostly cast as a classification problem. However, stance decisions involve complex judgments, which can be nowadays generated by prompting Large Language Models (LLMs). In this paper we present a new metho... | 2024.acl-long.49 | 10.18653/v1/2024.acl-long.49 | null | null | null |
2024.acl-long.50 | VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks | https://aclanthology.org/2024.acl-long.50/ | [
"Jing Yu Koh",
"Robert Lo",
"Lawrence Jang",
"Vikram Duvvur",
"Ming Lim",
"Po-Yu Huang",
"Graham Neubig",
"Shuyan Zhou",
"Russ Salakhutdinov",
"Daniel Fried"
] | 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... | 2024.acl-long.50 | 10.18653/v1/2024.acl-long.50 | null | 2401.13649 | title_snapshot |
2024.acl-long.51 | FineSurE: Fine-grained Summarization Evaluation using LLMs | https://aclanthology.org/2024.acl-long.51/ | [
"Hwanjun Song",
"Hang Su",
"Igor Shalyminov",
"Jason Cai",
"Saab Mansour"
] | 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... | 2024.acl-long.51 | 10.18653/v1/2024.acl-long.51 | null | 2407.00908 | title_snapshot |
2024.acl-long.52 | Tuning Large Multimodal Models for Videos using Reinforcement Learning from AI Feedback | https://aclanthology.org/2024.acl-long.52/ | [
"Daechul Ahn",
"Yura Choi",
"Youngjae Yu",
"Dongyeop Kang",
"Jonghyun Choi"
] | 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 ... | 2024.acl-long.52 | 10.18653/v1/2024.acl-long.52 | null | 2402.03746 | title_snapshot |
2024.acl-long.53 | Prompt Refinement with Image Pivot for Text-to-Image Generation | https://aclanthology.org/2024.acl-long.53/ | [
"Jingtao Zhan",
"Qingyao Ai",
"Yiqun Liu",
"Yingwei Pan",
"Ting Yao",
"Jiaxin Mao",
"Shaoping Ma",
"Tao Mei"
] | 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... | 2024.acl-long.53 | 10.18653/v1/2024.acl-long.53 | null | 2407.00247 | title_snapshot |
2024.acl-long.54 | Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation | https://aclanthology.org/2024.acl-long.54/ | [
"Masato Mita",
"Soichiro Murakami",
"Akihiko Kato",
"Peinan Zhang"
] | 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... | 2024.acl-long.54 | 10.18653/v1/2024.acl-long.54 | null | 2309.12030 | title_snapshot |
2024.acl-long.55 | AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation | https://aclanthology.org/2024.acl-long.55/ | [
"Zhaowei Wang",
"Wei Fan",
"Qing Zong",
"Hongming Zhang",
"Sehyun Choi",
"Tianqing Fang",
"Xin Liu",
"Yangqiu Song",
"Ginny Wong",
"Simon See"
] | 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... | 2024.acl-long.55 | 10.18653/v1/2024.acl-long.55 | null | 2402.10646 | title_snapshot |
2024.acl-long.56 | Reflect-RL: Two-Player Online RL Fine-Tuning for LMs | https://aclanthology.org/2024.acl-long.56/ | [
"Runlong Zhou",
"Simon Du",
"Beibin Li"
] | 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... | 2024.acl-long.56 | 10.18653/v1/2024.acl-long.56 | null | 2402.12621 | title_snapshot |
2024.acl-long.57 | Can ChatGPT’s Performance be Improved on Verb Metaphor Detection Tasks? Bootstrapping and Combining Tacit Knowledge | https://aclanthology.org/2024.acl-long.57/ | [
"Cheng Yang",
"Puli Chen",
"Qingbao Huang"
] | Metaphors detection, as an important task in the field of NLP, has been receiving sustained academic attention in recent years. Current researches focus supervised metaphors detection systems, which usually require large-scale, high-quality labeled data support. The emerge of large language models (e.g., ChatGPT) has m... | 2024.acl-long.57 | 10.18653/v1/2024.acl-long.57 | null | null | null |
2024.acl-long.58 | Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning | https://aclanthology.org/2024.acl-long.58/ | [
"Zhaorui Yang",
"Tianyu Pang",
"Haozhe Feng",
"Han Wang",
"Wei Chen",
"Minfeng Zhu",
"Qian Liu"
] | 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... | 2024.acl-long.58 | 10.18653/v1/2024.acl-long.58 | null | 2402.13669 | title_snapshot |
2024.acl-long.59 | An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation | https://aclanthology.org/2024.acl-long.59/ | [
"Kun Zhu",
"Xiaocheng Feng",
"Xiyuan Du",
"Yuxuan Gu",
"Weijiang Yu",
"Haotian Wang",
"Qianglong Chen",
"Zheng Chu",
"Jingchang Chen",
"Bing Qin"
] | 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... | 2024.acl-long.59 | 10.18653/v1/2024.acl-long.59 | null | 2406.01549 | title_snapshot |
2024.acl-long.60 | RORA: Robust Free-Text Rationale Evaluation | https://aclanthology.org/2024.acl-long.60/ | [
"Zhengping Jiang",
"Yining Lu",
"Hanjie Chen",
"Daniel Khashabi",
"Benjamin Van Durme",
"Anqi Liu"
] | 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... | 2024.acl-long.60 | 10.18653/v1/2024.acl-long.60 | null | 2402.18678 | title_snapshot |
2024.acl-long.61 | Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents | https://aclanthology.org/2024.acl-long.61/ | [
"Cheng Qian",
"Bingxiang He",
"Zhong Zhuang",
"Jia Deng",
"Yujia Qin",
"Xin Cong",
"Zhong Zhang",
"Jie Zhou",
"Yankai Lin",
"Zhiyuan Liu",
"Maosong Sun"
] | 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... | 2024.acl-long.61 | 10.18653/v1/2024.acl-long.61 | null | 2402.09205 | title_snapshot |
2024.acl-long.62 | InstructProtein: Aligning Human and Protein Language via Knowledge Instruction | https://aclanthology.org/2024.acl-long.62/ | [
"Zeyuan Wang",
"Qiang Zhang",
"Keyan Ding",
"Ming Qin",
"Xiang Zhuang",
"Xiaotong Li",
"Huajun Chen"
] | Large Language Models (LLMs) have revolutionized the field of natural language processing, but they fall short in comprehending biological sequences such as proteins. To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein l... | 2024.acl-long.62 | 10.18653/v1/2024.acl-long.62 | null | 2310.03269 | title_snapshot |
2024.acl-long.63 | ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models | https://aclanthology.org/2024.acl-long.63/ | [
"Aparna Elangovan",
"Ling Liu",
"Lei Xu",
"Sravan Babu Bodapati",
"Dan Roth"
] | 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 ... | 2024.acl-long.63 | 10.18653/v1/2024.acl-long.63 | null | 2405.18638 | title_snapshot |
2024.acl-long.64 | Linguistically Conditioned Semantic Textual Similarity | https://aclanthology.org/2024.acl-long.64/ | [
"Jingxuan Tu",
"Keer Xu",
"Liulu Yue",
"Bingyang Ye",
"Kyeongmin Rim",
"James Pustejovsky"
] | 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 ... | 2024.acl-long.64 | 10.18653/v1/2024.acl-long.64 | null | 2406.03673 | title_snapshot |
2024.acl-long.65 | Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future | https://aclanthology.org/2024.acl-long.65/ | [
"Zheng Chu",
"Jingchang Chen",
"Qianglong Chen",
"Weijiang Yu",
"Tao He",
"Haotian Wang",
"Weihua Peng",
"Ming Liu",
"Bing Qin",
"Ting Liu"
] | 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... | 2024.acl-long.65 | 10.18653/v1/2024.acl-long.65 | null | 2309.15402 | title_snapshot |
2024.acl-long.66 | TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models | https://aclanthology.org/2024.acl-long.66/ | [
"Zheng Chu",
"Jingchang Chen",
"Qianglong Chen",
"Weijiang Yu",
"Haotian Wang",
"Ming Liu",
"Bing Qin"
] | 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... | 2024.acl-long.66 | 10.18653/v1/2024.acl-long.66 | null | 2311.17667 | title_snapshot |
2024.acl-long.67 | BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering | https://aclanthology.org/2024.acl-long.67/ | [
"Zheng Chu",
"Jingchang Chen",
"Qianglong Chen",
"Haotian Wang",
"Kun Zhu",
"Xiyuan Du",
"Weijiang Yu",
"Ming Liu",
"Bing Qin"
] | 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... | 2024.acl-long.67 | 10.18653/v1/2024.acl-long.67 | null | 2406.19820 | title_snapshot |
2024.acl-long.68 | ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base | https://aclanthology.org/2024.acl-long.68/ | [
"Siyu Yuan",
"Jiangjie Chen",
"Changzhi Sun",
"Jiaqing Liang",
"Yanghua Xiao",
"Deqing Yang"
] | 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... | 2024.acl-long.68 | 10.18653/v1/2024.acl-long.68 | null | 2305.05994 | title_snapshot |
2024.acl-long.69 | TaSL: Continual Dialog State Tracking via Task Skill Localization and Consolidation | https://aclanthology.org/2024.acl-long.69/ | [
"Yujie Feng",
"Xu Chu",
"Yongxin Xu",
"Guangyuan Shi",
"Bo Liu",
"Xiao-Ming Wu"
] | 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... | 2024.acl-long.69 | 10.18653/v1/2024.acl-long.69 | null | 2408.09857 | title_snapshot |
2024.acl-long.70 | DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models | https://aclanthology.org/2024.acl-long.70/ | [
"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"
] | 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... | 2024.acl-long.70 | 10.18653/v1/2024.acl-long.70 | null | 2401.06066 | title_snapshot |
2024.acl-long.71 | Grounding Language Model with Chunking-Free In-Context Retrieval | https://aclanthology.org/2024.acl-long.71/ | [
"Hongjin Qian",
"Zheng Liu",
"Kelong Mao",
"Yujia Zhou",
"Zhicheng Dou"
] | 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... | 2024.acl-long.71 | 10.18653/v1/2024.acl-long.71 | null | 2402.09760 | title_snapshot |
2024.acl-long.72 | Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation | https://aclanthology.org/2024.acl-long.72/ | [
"Jiaxin Bai",
"Yicheng Wang",
"Tianshi Zheng",
"Yue Guo",
"Xin Liu",
"Yangqiu Song"
] | 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... | 2024.acl-long.72 | 10.18653/v1/2024.acl-long.72 | null | 2312.15643 | title_snapshot |
2024.acl-long.73 | Active Prompting with Chain-of-Thought for Large Language Models | https://aclanthology.org/2024.acl-long.73/ | [
"Shizhe Diao",
"Pengcheng Wang",
"Yong Lin",
"Rui Pan",
"Xiang Liu",
"Tong Zhang"
] | 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... | 2024.acl-long.73 | 10.18653/v1/2024.acl-long.73 | null | 2302.12246 | title_snapshot |
2024.acl-long.74 | EasyGen: Easing Multimodal Generation with BiDiffuser and LLMs | https://aclanthology.org/2024.acl-long.74/ | [
"Xiangyu Zhao",
"Bo Liu",
"Qijiong Liu",
"Guangyuan Shi",
"Xiao-Ming Wu"
] | 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... | 2024.acl-long.74 | 10.18653/v1/2024.acl-long.74 | null | 2310.08949 | title_snapshot |
2024.acl-long.75 | Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search | https://aclanthology.org/2024.acl-long.75/ | [
"Haochen Li",
"Xin Zhou",
"Zhiqi Shen"
] | 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... | 2024.acl-long.75 | 10.18653/v1/2024.acl-long.75 | null | 2401.04514 | title_snapshot |
2024.acl-long.76 | A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications | https://aclanthology.org/2024.acl-long.76/ | [
"Naomi Baes",
"Nick Haslam",
"Ekaterina Vylomova"
] | 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... | 2024.acl-long.76 | 10.18653/v1/2024.acl-long.76 | null | 2406.06052 | title_snapshot |
2024.acl-long.77 | Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal | https://aclanthology.org/2024.acl-long.77/ | [
"Jianheng Huang",
"Leyang Cui",
"Ante Wang",
"Chengyi Yang",
"Xinting Liao",
"Linfeng Song",
"Junfeng Yao",
"Jinsong Su"
] | 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... | 2024.acl-long.77 | 10.18653/v1/2024.acl-long.77 | null | 2403.01244 | title_snapshot |
2024.acl-long.78 | Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency | https://aclanthology.org/2024.acl-long.78/ | [
"Baizhou Huang",
"Shuai Lu",
"Xiaojun Wan",
"Nan Duan"
] | 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... | 2024.acl-long.78 | 10.18653/v1/2024.acl-long.78 | null | 2309.17272 | title_snapshot |
2024.acl-long.79 | Citation-Enhanced Generation for LLM-based Chatbots | https://aclanthology.org/2024.acl-long.79/ | [
"Weitao Li",
"Junkai Li",
"Weizhi Ma",
"Yang Liu"
] | 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... | 2024.acl-long.79 | 10.18653/v1/2024.acl-long.79 | null | 2402.16063 | title_snapshot |
2024.acl-long.80 | Transitive Consistency Constrained Learning for Entity-to-Entity Stance Detection | https://aclanthology.org/2024.acl-long.80/ | [
"Haoyang Wen",
"Eduard Hovy",
"Alexander Hauptmann"
] | 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... | 2024.acl-long.80 | 10.18653/v1/2024.acl-long.80 | null | null | null |
2024.acl-long.81 | Feature-Adaptive and Data-Scalable In-Context Learning | https://aclanthology.org/2024.acl-long.81/ | [
"Jiahao Li",
"Quan Wang",
"Licheng Zhang",
"Guoqing Jin",
"Zhendong Mao"
] | 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... | 2024.acl-long.81 | 10.18653/v1/2024.acl-long.81 | null | 2405.10738 | title_snapshot |
2024.acl-long.82 | Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games | https://aclanthology.org/2024.acl-long.82/ | [
"Yizhe Zhang",
"Jiarui Lu",
"Navdeep Jaitly"
] | 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... | 2024.acl-long.82 | 10.18653/v1/2024.acl-long.82 | null | 2310.01468 | title_snapshot |
2024.acl-long.83 | WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models | https://aclanthology.org/2024.acl-long.83/ | [
"Shangqing Tu",
"Yuliang Sun",
"Yushi Bai",
"Jifan Yu",
"Lei Hou",
"Juanzi Li"
] | 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... | 2024.acl-long.83 | 10.18653/v1/2024.acl-long.83 | null | 2311.07138 | title_snapshot |
2024.acl-long.84 | Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models | https://aclanthology.org/2024.acl-long.84/ | [
"Yida Zhao",
"Chao Lou",
"Kewei Tu"
] | 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... | 2024.acl-long.84 | 10.18653/v1/2024.acl-long.84 | null | 2407.17406 | title_snapshot |
2024.acl-long.85 | A Non-autoregressive Generation Framework for End-to-End Simultaneous Speech-to-Any Translation | https://aclanthology.org/2024.acl-long.85/ | [
"Zhengrui Ma",
"Qingkai Fang",
"Shaolei Zhang",
"Shoutao Guo",
"Yang Feng",
"Min Zhang"
] | 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... | 2024.acl-long.85 | 10.18653/v1/2024.acl-long.85 | null | 2406.06937 | title_judge |
2024.acl-long.86 | Probing Language Models for Pre-training Data Detection | https://aclanthology.org/2024.acl-long.86/ | [
"Zhenhua Liu",
"Tong Zhu",
"Chuanyuan Tan",
"Bing Liu",
"Haonan Lu",
"Wenliang Chen"
] | 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... | 2024.acl-long.86 | 10.18653/v1/2024.acl-long.86 | null | 2406.01333 | title_snapshot |
2024.acl-long.87 | Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding | https://aclanthology.org/2024.acl-long.87/ | [
"Zhihan Zhang",
"Yixin Cao",
"Chenchen Ye",
"Yunshan Ma",
"Lizi Liao",
"Tat-Seng Chua"
] | 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... | 2024.acl-long.87 | 10.18653/v1/2024.acl-long.87 | null | 2406.02472 | title_snapshot |
2024.acl-long.88 | IBSEN: Director-Actor Agent Collaboration for Controllable and Interactive Drama Script Generation | https://aclanthology.org/2024.acl-long.88/ | [
"Senyu Han",
"Lu Chen",
"Li-Min Lin",
"Zhengshan Xu",
"Kai Yu"
] | 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 ... | 2024.acl-long.88 | 10.18653/v1/2024.acl-long.88 | null | 2407.01093 | title_snapshot |
2024.acl-long.89 | Language Model Adaption for Reinforcement Learning with Natural Language Action Space | https://aclanthology.org/2024.acl-long.89/ | [
"Jiangxing Wang",
"Jiachen Li",
"Xiao Han",
"Deheng Ye",
"Zongqing Lu"
] | Reinforcement learning with natural language action space often suffers from the curse of dimensionality due to the combinatorial nature of the natural language. Previous research leverages pretrained language models to capture action semantics and reduce the size of the action space. However, since pretrained models a... | 2024.acl-long.89 | 10.18653/v1/2024.acl-long.89 | null | null | null |
2024.acl-long.90 | Evaluating Intention Detection Capability of Large Language Models in Persuasive Dialogues | https://aclanthology.org/2024.acl-long.90/ | [
"Hiromasa Sakurai",
"Yusuke Miyao"
] | We investigate intention detection in persuasive multi-turn dialogs employing the largest available Large Language Models (LLMs).Much of the prior research measures the intention detection capability of machine learning models without considering the conversational history.To evaluate LLMs’ intention detection capabili... | 2024.acl-long.90 | 10.18653/v1/2024.acl-long.90 | null | null | null |
2024.acl-long.91 | LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression | https://aclanthology.org/2024.acl-long.91/ | [
"Huiqiang Jiang",
"Qianhui Wu",
"Xufang Luo",
"Dongsheng Li",
"Chin-Yew Lin",
"Yuqing Yang",
"Lili Qiu"
] | 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... | 2024.acl-long.91 | 10.18653/v1/2024.acl-long.91 | null | 2310.06839 | title_snapshot |
2024.acl-long.92 | Persuading across Diverse Domains: a Dataset and Persuasion Large Language Model | https://aclanthology.org/2024.acl-long.92/ | [
"Chuhao Jin",
"Kening Ren",
"Lingzhen Kong",
"Xiting Wang",
"Ruihua Song",
"Huan Chen"
] | Persuasive dialogue requires multi-turn following and planning abilities to achieve the goal of persuading users, which is still challenging even for state-of-the-art large language models (LLMs). Previous works focus on retrieval-based models or generative models in a specific domain due to a lack of data across multi... | 2024.acl-long.92 | 10.18653/v1/2024.acl-long.92 | null | null | null |
2024.acl-long.93 | HealMe: Harnessing Cognitive Reframing in Large Language Models for Psychotherapy | https://aclanthology.org/2024.acl-long.93/ | [
"Mengxi Xiao",
"Qianqian Xie",
"Ziyan Kuang",
"Zhicheng Liu",
"Kailai Yang",
"Min Peng",
"Weiguang Han",
"Jimin Huang"
] | 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,... | 2024.acl-long.93 | 10.18653/v1/2024.acl-long.93 | null | 2403.05574 | title_snapshot |
2024.acl-long.94 | Multimodal Prompt Learning with Missing Modalities for Sentiment Analysis and Emotion Recognition | https://aclanthology.org/2024.acl-long.94/ | [
"Zirun Guo",
"Tao Jin",
"Zhou Zhao"
] | 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... | 2024.acl-long.94 | 10.18653/v1/2024.acl-long.94 | null | 2407.05374 | title_snapshot |
2024.acl-long.95 | An Effective Pronunciation Assessment Approach Leveraging Hierarchical Transformers and Pre-training Strategies | https://aclanthology.org/2024.acl-long.95/ | [
"Bi-Cheng Yan",
"Jiun-Ting Li",
"Yi-Cheng Wang",
"Hsin Wei Wang",
"Tien-Hong Lo",
"Yung-Chang Hsu",
"Wei-Cheng Chao",
"Berlin Chen"
] | 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,... | 2024.acl-long.95 | 10.18653/v1/2024.acl-long.95 | null | null | null |
2024.acl-long.96 | Detection-Correction Structure via General Language Model for Grammatical Error Correction | https://aclanthology.org/2024.acl-long.96/ | [
"Wei Li",
"Houfeng Wang"
] | 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... | 2024.acl-long.96 | 10.18653/v1/2024.acl-long.96 | null | 2405.17804 | title_snapshot |
2024.acl-long.97 | Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer | https://aclanthology.org/2024.acl-long.97/ | [
"Yongxin Zhu",
"Dan Su",
"Liqiang He",
"Linli Xu",
"Dong Yu"
] | 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... | 2024.acl-long.97 | 10.18653/v1/2024.acl-long.97 | null | 2406.00976 | title_snapshot |
2024.acl-long.98 | Selene: Pioneering Automated Proof in Software Verification | https://aclanthology.org/2024.acl-long.98/ | [
"Lichen Zhang",
"Shuai Lu",
"Nan Duan"
] | 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... | 2024.acl-long.98 | 10.18653/v1/2024.acl-long.98 | null | 2401.07663 | title_snapshot |
2024.acl-long.99 | Dissecting Human and LLM Preferences | https://aclanthology.org/2024.acl-long.99/ | [
"Junlong Li",
"Fan Zhou",
"Shichao Sun",
"Yikai Zhang",
"Hai Zhao",
"Pengfei Liu"
] | 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... | 2024.acl-long.99 | 10.18653/v1/2024.acl-long.99 | null | 2402.11296 | title_snapshot |
2024.acl-long.100 | UniCoder: Scaling Code Large Language Model via Universal Code | https://aclanthology.org/2024.acl-long.100/ | [
"Tao Sun",
"Linzheng Chai",
"Jian Yang",
"Yuwei Yin",
"Hongcheng Guo",
"Jiaheng Liu",
"Bing Wang",
"Liqun Yang",
"Zhoujun Li"
] | 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... | 2024.acl-long.100 | 10.18653/v1/2024.acl-long.100 | null | 2406.16441 | title_snapshot |
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