NAACL
Collection
Accepted papers for NAACL (Annual Conference of the North American Chapter of the Association for Computational Linguistics), one dataset per year. • 9 items • Updated
paper_id stringlengths 17 19 | title stringlengths 32 151 | paper_url stringlengths 43 45 | authors listlengths 1 58 | abstract large_stringlengths 469 1.92k | anthology_id stringlengths 17 19 | doi stringlengths 29 31 | award stringclasses 0
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|
2024.naacl-long.1 | Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences | https://aclanthology.org/2024.naacl-long.1/ | [
"Hongyi Liu",
"Qingyun Wang",
"Payam Karisani",
"Heng Ji"
] | Named entity recognition is a key component of Information Extraction (IE), particularly in scientific domains such as biomedicine and chemistry, where large language models (LLMs), e.g., ChatGPT, fall short. We investigate the applicability of transfer learning for enhancing a named entity recognition model trained in... | 2024.naacl-long.1 | 10.18653/v1/2024.naacl-long.1 | null | 2401.10472 | title_snapshot |
2024.naacl-long.2 | Text Diffusion Model with Encoder-Decoder Transformers for Sequence-to-Sequence Generation | https://aclanthology.org/2024.naacl-long.2/ | [
"Hongyi Yuan",
"Zheng Yuan",
"Chuanqi Tan",
"Fei Huang",
"Songfang Huang"
] | The diffusion model, a new generative modeling paradigm, has achieved great success in image, audio, and video generation.However, considering the discrete categorical nature of the text, it is not trivial to extend continuous diffusion models to natural language. In this work, we propose SeqDiffuSeq, a text diffusion ... | 2024.naacl-long.2 | 10.18653/v1/2024.naacl-long.2 | null | 2212.10325 | title_judge |
2024.naacl-long.3 | An Interactive Framework for Profiling News Media Sources | https://aclanthology.org/2024.naacl-long.3/ | [
"Nikhil Mehta",
"Dan Goldwasser"
] | The recent rise of social media has led to the spread of large amounts of fake and biased news, content published with the intent to sway beliefs. While detecting and profiling the sources that spread this news is important to maintain a healthy society, it is challenging for automated systems.In this paper, we propose... | 2024.naacl-long.3 | 10.18653/v1/2024.naacl-long.3 | null | 2309.07384 | title_snapshot |
2024.naacl-long.4 | Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study | https://aclanthology.org/2024.naacl-long.4/ | [
"Yinghao Li",
"Haorui Wang",
"Chao Zhang"
] | Large Language Models (LLMs) have shown remarkable proficiency in language understanding and have been successfully applied to a variety of real-world tasks through task-specific fine-tuning or prompt engineering. Despite these advancements, it remains an open question whether LLMs are fundamentally capable of reasonin... | 2024.naacl-long.4 | 10.18653/v1/2024.naacl-long.4 | null | 2311.07387 | title_snapshot |
2024.naacl-long.5 | TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in Conversation | https://aclanthology.org/2024.naacl-long.5/ | [
"Taeyang Yun",
"Hyunkuk Lim",
"Jeonghwan Lee",
"Min Song"
] | Emotion Recognition in Conversation (ERC) plays a crucial role in enabling dialogue sys- tems to effectively respond to user requests. The emotions in a conversation can be identi- fied by the representations from various modal- ities, such as audio, visual, and text. How- ever, due to the weak contribution of non-verb... | 2024.naacl-long.5 | 10.18653/v1/2024.naacl-long.5 | null | 2401.12987 | title_snapshot |
2024.naacl-long.6 | Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries | https://aclanthology.org/2024.naacl-long.6/ | [
"Seanie Lee",
"Jianpeng Cheng",
"Joris Driesen",
"Alexandru Coca",
"Anders Johannsen"
] | Few-shot dialogue state tracking (DST) with Large Language Models (LLM) relies on an effective and efficient conversation retriever to find similar in-context examples for prompt learning. Previous works use raw dialogue context as search keys and queries, and a retriever is fine-tuned with annotated dialogues to achie... | 2024.naacl-long.6 | 10.18653/v1/2024.naacl-long.6 | null | 2402.13043 | title_snapshot |
2024.naacl-long.7 | Promptly Predicting Structures: The Return of Inference | https://aclanthology.org/2024.naacl-long.7/ | [
"Maitrey Mehta",
"Valentina Pyatkin",
"Vivek Srikumar"
] | Prompt-based methods have been used extensively across NLP to build zero- and few-shot label predictors. Many NLP tasks are naturally structured: that is, their outputs consist of multiple labels which constrain each other. Annotating data for such tasks can be cumbersome. Can the promise of the prompt-based paradigm b... | 2024.naacl-long.7 | 10.18653/v1/2024.naacl-long.7 | null | 2401.06877 | title_snapshot |
2024.naacl-long.8 | On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL | https://aclanthology.org/2024.naacl-long.8/ | [
"Yutong Shao",
"Ndapa Nakashole"
] | Structured data, prevalent in tables, databases, and knowledge graphs, poses a significant challenge in its representation. With the advent of large language models (LLMs), there has been a shift towards linearization-based methods, which process structured data as sequential token streams, diverging from approaches th... | 2024.naacl-long.8 | 10.18653/v1/2024.naacl-long.8 | null | 2404.02389 | title_snapshot |
2024.naacl-long.9 | Extractive Summarization with Text Generator | https://aclanthology.org/2024.naacl-long.9/ | [
"Thang Le",
"Anh Tuan Luu"
] | Standard extractive systems suffer from the lack of gold training signals since existing corpora solely provide document and human-written summary pairs while disregarding extractive labels. As a result, existing methods resort to imperfect pseudo-labels that are both biased and error-prone, thereby hindering the learn... | 2024.naacl-long.9 | 10.18653/v1/2024.naacl-long.9 | null | null | null |
2024.naacl-long.10 | Self-generated Replay Memories for Continual Neural Machine Translation | https://aclanthology.org/2024.naacl-long.10/ | [
"Michele Resta",
"Davide Bacciu"
] | Modern Neural Machine Translation systems exhibit strong performance in several different languages and are constantly improving. Their ability to learn continuously is, however, still severely limited by the catastrophic forgetting issue. In this work, we leverage a key property of encoder-decoder Transformers, i.e. t... | 2024.naacl-long.10 | 10.18653/v1/2024.naacl-long.10 | null | 2403.13130 | title_snapshot |
2024.naacl-long.11 | Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models | https://aclanthology.org/2024.naacl-long.11/ | [
"Yangyi Chen",
"Karan Sikka",
"Michael Cogswell",
"Heng Ji",
"Ajay Divakaran"
] | Vision-language models (VLMs) have recently demonstrated strong efficacy as visual assistants that can parse natural queries about the visual content and generate human-like outputs. In this work, we explore the ability of these models to demonstrate human-like reasoning based on the perceived information. To address a... | 2024.naacl-long.11 | 10.18653/v1/2024.naacl-long.11 | null | 2309.04461 | title_snapshot |
2024.naacl-long.12 | Building Knowledge-Guided Lexica to Model Cultural Variation | https://aclanthology.org/2024.naacl-long.12/ | [
"Shreya Havaldar",
"Salvatore Giorgi",
"Sunny Rai",
"Thomas Talhelm",
"Sharath Chandra Guntuku",
"Lyle Ungar"
] | Cultural variation exists between nations (e.g., the United States vs. China), but also within regions (e.g., California vs. Texas, Los Angeles vs. San Francisco). Measuring this regional cultural variation can illuminate how and why people think and behave differently. Historically, it has been difficult to computatio... | 2024.naacl-long.12 | 10.18653/v1/2024.naacl-long.12 | null | 2406.11622 | title_snapshot |
2024.naacl-long.13 | Adaptive Rank Selections for Low-Rank Approximation of Language Models | https://aclanthology.org/2024.naacl-long.13/ | [
"Shangqian Gao",
"Ting Hua",
"Yen-Chang Hsu",
"Yilin Shen",
"Hongxia Jin"
] | Singular Value Decomposition (SVD) or its weighted variants has significantly progressed in compressing language models. Previous works assume the same importance for all operations and assign the same number of ranks for different layers in a language model. However, such a uniform rank selection is sub-optimal since ... | 2024.naacl-long.13 | 10.18653/v1/2024.naacl-long.13 | null | null | null |
2024.naacl-long.14 | An Empirical Study of Consistency Regularization for End-to-End Speech-to-Text Translation | https://aclanthology.org/2024.naacl-long.14/ | [
"Pengzhi Gao",
"Ruiqing Zhang",
"Zhongjun He",
"Hua Wu",
"Haifeng Wang"
] | Consistency regularization methods, such as R-Drop (Liang et al., 2021) and CrossConST (Gao et al., 2023), have achieved impressive supervised and zero-shot performance in the neural machine translation (NMT) field. Can we also boost end-to-end (E2E) speech-to-text translation (ST) by leveraging consistency regularizat... | 2024.naacl-long.14 | 10.18653/v1/2024.naacl-long.14 | null | 2308.14482 | title_snapshot |
2024.naacl-long.15 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | https://aclanthology.org/2024.naacl-long.15/ | [
"Zhenhailong Wang",
"Shaoguang Mao",
"Wenshan Wu",
"Tao Ge",
"Furu Wei",
"Heng Ji"
] | Human intelligence thrives on cognitive synergy, where collaboration among different minds yield superior outcomes compared to isolated individuals. In this work, we propose Solo Performance Prompting (SPP), which transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multi... | 2024.naacl-long.15 | 10.18653/v1/2024.naacl-long.15 | null | 2307.05300 | title_snapshot |
2024.naacl-long.16 | FPT: Feature Prompt Tuning for Few-shot Readability Assessment | https://aclanthology.org/2024.naacl-long.16/ | [
"Ziyang Wang",
"Sanwoo Lee",
"Hsiu-Yuan Huang",
"Yunfang Wu"
] | Prompt-based methods have achieved promising results in most few-shot text classification tasks. However, for readability assessment tasks, traditional prompt methods lack crucial linguistic knowledge, which has already been proven to be essential.Moreover, previous studies on utilizing linguistic features have shown n... | 2024.naacl-long.16 | 10.18653/v1/2024.naacl-long.16 | null | 2404.02772 | title_snapshot |
2024.naacl-long.17 | Self-Prompting Large Language Models for Zero-Shot Open-Domain QA | https://aclanthology.org/2024.naacl-long.17/ | [
"Junlong Li",
"Jinyuan Wang",
"Zhuosheng Zhang",
"Hai Zhao"
] | Open-Domain Question Answering (ODQA) aims to answer questions without explicitly providing specific background documents. This task becomes notably challenging in a zero-shot setting where no data is available to train tailored retrieval-reader models.While recent Large Language Models (LLMs) like GPT-3 have demonstra... | 2024.naacl-long.17 | 10.18653/v1/2024.naacl-long.17 | null | 2212.08635 | title_snapshot |
2024.naacl-long.18 | Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs? | https://aclanthology.org/2024.naacl-long.18/ | [
"Kai Sun",
"Yifan Xu",
"Hanwen Zha",
"Yue Liu",
"Xin Luna Dong"
] | Since the recent prosperity of Large Language Models (LLMs), there have been interleaved discussions regarding how to reduce hallucinations from LLM responses, how to increase the factuality of LLMs, and whether Knowledge Graphs (KGs), which store the world knowledge in a symbolic form, will be replaced with LLMs. In t... | 2024.naacl-long.18 | 10.18653/v1/2024.naacl-long.18 | null | 2308.10168 | title_snapshot |
2024.naacl-long.19 | kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning | https://aclanthology.org/2024.naacl-long.19/ | [
"Wenting Zhao",
"Ye Liu",
"Yao Wan",
"Yibo Wang",
"Qingyang Wu",
"Zhongfen Deng",
"Jiangshu Du",
"Shuaiqi Liu",
"Yunlong Xu",
"Philip Yu"
] | Task-Oriented Parsing (TOP) enables conversational assistants to interpret user commands expressed in natural language, transforming them into structured outputs that combine elements of both natural language and intent/slot tags. Recently, Large Language Models (LLMs) have achieved impressive performance in synthesizi... | 2024.naacl-long.19 | 10.18653/v1/2024.naacl-long.19 | null | 2312.10771 | title_snapshot |
2024.naacl-long.20 | ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems | https://aclanthology.org/2024.naacl-long.20/ | [
"Jon Saad-Falcon",
"Omar Khattab",
"Christopher Potts",
"Matei Zaharia"
] | Evaluating retrieval-augmented generation (RAG) systems traditionally relies on hand annotations for input queries, passages to retrieve, and responses to generate. We introduce ARES, an Automated RAG Evaluation System, for evaluating RAG systems along the dimensions of context relevance, answer faithfulness, and answe... | 2024.naacl-long.20 | 10.18653/v1/2024.naacl-long.20 | null | 2311.09476 | title_snapshot |
2024.naacl-long.21 | DEMO: A Statistical Perspective for Efficient Image-Text Matching | https://aclanthology.org/2024.naacl-long.21/ | [
"Fan Zhang",
"Xian-Sheng Hua",
"Chong Chen",
"Xiao Luo"
] | Image-text matching has been a long-standing problem, which seeks to connect vision and language through semantic understanding. Due to the capability to manage large-scale raw data, unsupervised hashing-based approaches have gained prominence recently. They typically construct a semantic similarity structure using the... | 2024.naacl-long.21 | 10.18653/v1/2024.naacl-long.21 | null | 2405.11496 | title_snapshot |
2024.naacl-long.22 | SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning | https://aclanthology.org/2024.naacl-long.22/ | [
"Bin Wang",
"Zhengyuan Liu",
"Xin Huang",
"Fangkai Jiao",
"Yang Ding",
"AiTi Aw",
"Nancy Chen"
] | We present SeaEval, a benchmark for multilingual foundation models. In addition to characterizing how these models understand and reason with natural language, we also investigate how well they comprehend cultural practices, nuances, and values. Alongside standard accuracy metrics, we investigate the brittleness of fou... | 2024.naacl-long.22 | 10.18653/v1/2024.naacl-long.22 | null | 2309.04766 | title_snapshot |
2024.naacl-long.23 | Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision | https://aclanthology.org/2024.naacl-long.23/ | [
"Seongyun Lee",
"Sue Hyun Park",
"Yongrae Jo",
"Minjoon Seo"
] | Large multimodal models suffer from multimodal hallucination, where they provide incorrect responses misaligned with the given visual information. Recent works have conjectured that one of the reasons behind multimodal hallucination is due to the vision encoder failing to ground on the image properly. To mitigate this ... | 2024.naacl-long.23 | 10.18653/v1/2024.naacl-long.23 | null | 2311.07362 | title_snapshot |
2024.naacl-long.24 | LLMs Are Few-Shot In-Context Low-Resource Language Learners | https://aclanthology.org/2024.naacl-long.24/ | [
"Samuel Cahyawijaya",
"Holy Lovenia",
"Pascale Fung"
] | In-context learning (ICL) empowers large language models (LLMs) to perform diverse tasks in underrepresented languages using only short in-context information, offering a crucial avenue for narrowing the gap between high-resource and low-resource languages.Nonetheless, there is only a handful of works explored ICL for ... | 2024.naacl-long.24 | 10.18653/v1/2024.naacl-long.24 | null | 2403.16512 | title_snapshot |
2024.naacl-long.25 | Simple and effective data augmentation for compositional generalization | https://aclanthology.org/2024.naacl-long.25/ | [
"Yuekun Yao",
"Alexander Koller"
] | Compositional generalization, the ability to predict complex meanings from training on simpler sentences, poses challenges for powerful pretrained seq2seq models. In this paper, we show that data augmentation methods that sample MRs and backtranslate them can be effective for compositional generalization, but only if w... | 2024.naacl-long.25 | 10.18653/v1/2024.naacl-long.25 | null | 2401.09815 | title_snapshot |
2024.naacl-long.26 | Rethinking Tabular Data Understanding with Large Language Models | https://aclanthology.org/2024.naacl-long.26/ | [
"Tianyang Liu",
"Fei Wang",
"Muhao Chen"
] | Large Language Models (LLMs) have shown to be capable of various tasks, yet their capability in interpreting and reasoning over tabular data remains an underexplored area. In this context, this study investigates from three core perspectives: the robustness of LLMs to structural perturbations in tables, the comparative... | 2024.naacl-long.26 | 10.18653/v1/2024.naacl-long.26 | null | 2312.16702 | title_snapshot |
2024.naacl-long.27 | From Shortcuts to Triggers: Backdoor Defense with Denoised PoE | https://aclanthology.org/2024.naacl-long.27/ | [
"Qin Liu",
"Fei Wang",
"Chaowei Xiao",
"Muhao Chen"
] | Language models are often at risk of diverse backdoor attacks, especially data poisoning. Thus, it is important to investigate defense solutions for addressing them. Existing backdoor defense methods mainly focus on backdoor attacks with explicit triggers, leaving a universal defense against various backdoor attacks wi... | 2024.naacl-long.27 | 10.18653/v1/2024.naacl-long.27 | null | 2305.14910 | title_snapshot |
2024.naacl-long.28 | BookSQL: A Large Scale Text-to-SQL Dataset for Accounting Domain | https://aclanthology.org/2024.naacl-long.28/ | [
"Rahul Kumar",
"Amar Raja Dibbu",
"Shrutendra Harsola",
"Vignesh Subrahmaniam",
"Ashutosh Modi"
] | Several large-scale datasets (e.g., WikiSQL, Spider) for developing natural language interfaces to databases have recently been proposed. These datasets cover a wide breadth of domains but fall short on some essential domains, such as finance and accounting. Given that accounting databases are used worldwide, particula... | 2024.naacl-long.28 | 10.18653/v1/2024.naacl-long.28 | null | 2406.07860 | title_snapshot |
2024.naacl-long.29 | FLAP: Flow-Adhering Planning with Constrained Decoding in LLMs | https://aclanthology.org/2024.naacl-long.29/ | [
"Shamik Roy",
"Sailik Sengupta",
"Daniele Bonadiman",
"Saab Mansour",
"Arshit Gupta"
] | Planning is a crucial task for agents in task oriented dialogs (TODs). Human agents typically resolve user issues by following predefined workflows, decomposing workflow steps into actionable items, and performing actions by executing APIs in order; all of which require reasoning and planning. With the recent advances ... | 2024.naacl-long.29 | 10.18653/v1/2024.naacl-long.29 | null | 2403.05766 | title_snapshot |
2024.naacl-long.30 | DuRE: Dual Contrastive Self Training for Semi-Supervised Relation Extraction | https://aclanthology.org/2024.naacl-long.30/ | [
"Yuxi Feng",
"Laks Lakshmanan"
] | Document-level Relation Extraction (RE) aims to extract relation triples from documents. Existing document-RE models typically rely on supervised learning which requires substantial labeled data. To alleviate the amount of human supervision, Self-training (ST) has prospered again in language understanding by augmenting... | 2024.naacl-long.30 | 10.18653/v1/2024.naacl-long.30 | null | null | null |
2024.naacl-long.31 | Query-Efficient Textual Adversarial Example Generation for Black-Box Attacks | https://aclanthology.org/2024.naacl-long.31/ | [
"Zhen Yu",
"Zhenhua Chen",
"Kun He"
] | Deep neural networks for Natural Language Processing (NLP) have been demonstrated to be vulnerable to textual adversarial examples. Existing black-box attacks typically require thousands of queries on the target model, making them expensive in real-world applications. In this paper, we propose a new approach that guide... | 2024.naacl-long.31 | 10.18653/v1/2024.naacl-long.31 | null | null | null |
2024.naacl-long.32 | Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles | https://aclanthology.org/2024.naacl-long.32/ | [
"Kung-Hsiang Huang",
"Philippe Laban",
"Alexander Fabbri",
"Prafulla Kumar Choubey",
"Shafiq Joty",
"Caiming Xiong",
"Chien-Sheng Wu"
] | Previous research in multi-document news summarization has typically concentrated on collating information that all sources agree upon. However, the summarization of diverse information dispersed across multiple articles about an event remains underexplored. In this paper, we propose a new task of summarizing diverse i... | 2024.naacl-long.32 | 10.18653/v1/2024.naacl-long.32 | null | 2309.09369 | title_snapshot |
2024.naacl-long.33 | AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation | https://aclanthology.org/2024.naacl-long.33/ | [
"Haoyi Qiu",
"Kung-Hsiang Huang",
"Jingnong Qu",
"Nanyun Peng"
] | Ensuring factual consistency is crucial for natural language generation tasks, particularly in abstractive summarization, where preserving the integrity of information is paramount. Prior works on evaluating factual consistency of summarization often take the entailment-based approaches that first generate perturbed (f... | 2024.naacl-long.33 | 10.18653/v1/2024.naacl-long.33 | null | 2311.09521 | title_snapshot |
2024.naacl-long.34 | PILOT: Legal Case Outcome Prediction with Case Law | https://aclanthology.org/2024.naacl-long.34/ | [
"Lang Cao",
"Zifeng Wang",
"Cao Xiao",
"Jimeng Sun"
] | Machine learning shows promise in predicting the outcome of legal cases, but most research has concentrated on civil law cases rather than case law systems. We identified two unique challenges in making legal case outcome predictions with case law. First, it is crucial to identify relevant precedent cases that serve as... | 2024.naacl-long.34 | 10.18653/v1/2024.naacl-long.34 | null | 2401.15770 | title_snapshot |
2024.naacl-long.35 | ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models | https://aclanthology.org/2024.naacl-long.35/ | [
"Zequan Liu",
"Jiawen Lyn",
"Wei Zhu",
"Xing Tian",
"Yvette Graham"
] | Parameter-efficient fine-tuning (PEFT) is widely studied for its effectiveness and efficiency in the era of large language models. Low-rank adaptation (LoRA) has demonstrated commendable performance as a popular and representative method. However, it is implemented with a fixed intrinsic rank that might not be the idea... | 2024.naacl-long.35 | 10.18653/v1/2024.naacl-long.35 | null | 2403.16187 | title_snapshot |
2024.naacl-long.36 | R-Spin: Efficient Speaker and Noise-invariant Representation Learning with Acoustic Pieces | https://aclanthology.org/2024.naacl-long.36/ | [
"Heng-Jui Chang",
"James Glass"
] | This paper introduces Robust Spin (R-Spin), a data-efficient domain-specific self-supervision method for speaker and noise-invariant speech representations by learning discrete acoustic units with speaker-invariant clustering (Spin). R-Spin resolves Spin’s issues and enhances content representations by learning to pred... | 2024.naacl-long.36 | 10.18653/v1/2024.naacl-long.36 | null | 2311.09117 | title_snapshot |
2024.naacl-long.37 | InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions | https://aclanthology.org/2024.naacl-long.37/ | [
"Yifan Wang",
"Yafei Liu",
"Chufan Shi",
"Haoling Li",
"Chen Chen",
"Haonan Lu",
"Yujiu Yang"
] | Instruction tuning effectively optimizes Large Language Models (LLMs) for downstream tasks. Due to the changing environment in real-life applications, LLMs necessitate continual task-specific adaptation without catastrophic forgetting. Considering the heavy computational cost, replay-based Continual Learning (CL) metho... | 2024.naacl-long.37 | 10.18653/v1/2024.naacl-long.37 | null | 2403.11435 | title_snapshot |
2024.naacl-long.38 | Language Agnostic Code Embeddings | https://aclanthology.org/2024.naacl-long.38/ | [
"Saiteja Utpala",
"Alex Gu",
"Pin-Yu Chen"
] | Recently, code language models have achieved notable advancements in addressing a diverse array of essential code comprehension and generation tasks. Yet, the field lacks a comprehensive deep dive and understanding of the code embeddings of multilingual code models. In this paper, we present a comprehensive study on mu... | 2024.naacl-long.38 | 10.18653/v1/2024.naacl-long.38 | null | 2310.16803 | title_snapshot |
2024.naacl-long.39 | An Examination of the Compositionality of Large Generative Vision-Language Models | https://aclanthology.org/2024.naacl-long.39/ | [
"Teli Ma",
"Rong Li",
"Junwei Liang"
] | With the success of Large Language Models (LLMs), many Generative Vision-Language Models (GVLMs) have been constructed via multimodal instruction tuning. However, the performance of GVLMs in multimodal compositional reasoning remains under-explored. In this paper, we examine both the evaluation metrics ( VisualGPTScore... | 2024.naacl-long.39 | 10.18653/v1/2024.naacl-long.39 | null | 2308.10509 | title_snapshot |
2024.naacl-long.40 | Two Heads are Better than One: Nested PoE for Robust Defense Against Multi-Backdoors | https://aclanthology.org/2024.naacl-long.40/ | [
"Victoria Graf",
"Qin Liu",
"Muhao Chen"
] | Data poisoning backdoor attacks can cause undesirable behaviors in large language models (LLMs), and defending against them is of increasing importance. Existing defense mechanisms often assume that only one type of trigger is adopted by the attacker, while defending against multiple simultaneous and independent trigge... | 2024.naacl-long.40 | 10.18653/v1/2024.naacl-long.40 | null | 2404.02356 | title_snapshot |
2024.naacl-long.41 | VertAttack: Taking Advantage of Text Classifiers’ Horizontal Vision | https://aclanthology.org/2024.naacl-long.41/ | [
"Jonathan Rusert"
] | Text classification systems have continuouslyimproved in performance over the years. How-ever, nearly all current SOTA classifiers have asimilar shortcoming, they process text in a hor-izontal manner. Vertically written words willnot be recognized by a classifier. In contrast,humans are easily able to recognize and rea... | 2024.naacl-long.41 | 10.18653/v1/2024.naacl-long.41 | null | 2404.08538 | title_snapshot |
2024.naacl-long.42 | KDMCSE: Knowledge Distillation Multimodal Sentence Embeddings with Adaptive Angular margin Contrastive Learning | https://aclanthology.org/2024.naacl-long.42/ | [
"Cong-Duy Nguyen",
"Thong Nguyen",
"Xiaobao Wu",
"Anh Tuan Luu"
] | Previous work on multimodal sentence embedding has proposed multimodal contrastive learning and achieved promising results. However, by taking the rest of the batch as negative samples without reviewing when forming contrastive pairs, those studies encountered many suspicious and noisy negative examples, significantly ... | 2024.naacl-long.42 | 10.18653/v1/2024.naacl-long.42 | null | 2403.17486 | title_snapshot |
2024.naacl-long.43 | The taste of IPA: Towards open-vocabulary keyword spotting and forced alignment in any language | https://aclanthology.org/2024.naacl-long.43/ | [
"Jian Zhu",
"Changbing Yang",
"Farhan Samir",
"Jahurul Islam"
] | In this project, we demonstrate that phoneme-based models for speech processing can achieve strong crosslinguistic generalizability to unseen languages. We curated the IPAPACK, a massively multilingual speech corpora with phonemic transcriptions, encompassing more than 115 languages from diverse language families, sele... | 2024.naacl-long.43 | 10.18653/v1/2024.naacl-long.43 | null | 2311.08323 | title_snapshot |
2024.naacl-long.44 | Think Before You Act: A Two-Stage Framework for Mitigating Gender Bias Towards Vision-Language Tasks | https://aclanthology.org/2024.naacl-long.44/ | [
"Yunqi Zhang",
"Songda Li",
"Chunyuan Deng",
"Luyi Wang",
"Hui Zhao"
] | Gender bias in vision-language models (VLMs) can reinforce harmful stereotypes and discrimination. In this paper, we focus on mitigating gender bias towards vision-language tasks. We identify object hallucination as the essence of gender bias in VLMs. Existing VLMs tend to focus on salient or familiar attributes in ima... | 2024.naacl-long.44 | 10.18653/v1/2024.naacl-long.44 | null | 2405.16860 | title_snapshot |
2024.naacl-long.45 | BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings | https://aclanthology.org/2024.naacl-long.45/ | [
"Xianming Li",
"Jing Li"
] | Sentence embeddings are crucial in measuring semantic similarity. Most recent studies employed large language models (LLMs) to learn sentence embeddings. Existing LLMs mainly adopted autoregressive architecture without explicit backward dependency modeling. Therefore, we examined the effects of backward dependencies in... | 2024.naacl-long.45 | 10.18653/v1/2024.naacl-long.45 | null | 2311.05296 | title_snapshot |
2024.naacl-long.46 | Assessing Factual Reliability of Large Language Model Knowledge | https://aclanthology.org/2024.naacl-long.46/ | [
"Weixuan Wang",
"Barry Haddow",
"Alexandra Birch",
"Wei Peng"
] | The factual knowledge of LLMs is typically evaluated using accuracy, yet this metric does not capture the vulnerability of LLMs to hallucination-inducing factors like prompt and context variability. How do we evaluate the capabilities of LLMs to consistently produce factually correct answers? In this paper, we propose ... | 2024.naacl-long.46 | 10.18653/v1/2024.naacl-long.46 | null | null | null |
2024.naacl-long.47 | Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems | https://aclanthology.org/2024.naacl-long.47/ | [
"Zhenpeng Su",
"Xing W",
"Wei Zhou",
"Guangyuan Ma",
"Songlin Hu"
] | Dialogue response selection aims to select an appropriate response from several candidates based on a given user and system utterance history. Most existing works primarily focus on post-training and fine-tuning tailored for cross-encoders. However, there are no post-training methods tailored for dense encoders in dial... | 2024.naacl-long.47 | 10.18653/v1/2024.naacl-long.47 | null | 2306.04357 | title_snapshot |
2024.naacl-long.48 | Toolink: Linking Toolkit Creation and Using through Chain-of-Solving on Open-Source Model | https://aclanthology.org/2024.naacl-long.48/ | [
"Cheng Qian",
"Chenyan Xiong",
"Zhenghao Liu",
"Zhiyuan Liu"
] | Large Language Models (LLMs) have demonstrated remarkable progress in utilizing tools, but their closed-source nature and high inference costs pose limitations on their adaptability, necessitating a valid method that leverages smaller, open-sourced models. In this paper, we introduce Toolink, a comprehensive framework ... | 2024.naacl-long.48 | 10.18653/v1/2024.naacl-long.48 | null | 2310.05155 | title_snapshot |
2024.naacl-long.49 | Create! Don’t Repeat: A Paradigm Shift in Multi-Label Augmentation through Label Creative Generation | https://aclanthology.org/2024.naacl-long.49/ | [
"Letian Wang",
"Xianggen Liu",
"Jiancheng Lv"
] | We propose Label Creative Generation (LCG), a new paradigm in multi-label data augmentation. Beyond repeating data points with fixed labels, LCG creates new data by exploring innovative label combinations. Within LCG, we introduce Tail-Driven Conditional Augmentation (TDCA), combining tail-driven label sampling and lab... | 2024.naacl-long.49 | 10.18653/v1/2024.naacl-long.49 | null | null | null |
2024.naacl-long.50 | Neurocache: Efficient Vector Retrieval for Long-range Language Modeling | https://aclanthology.org/2024.naacl-long.50/ | [
"Ali Safaya",
"Deniz Yuret"
] | This paper introduces Neurocache, an approach to extend the effective context size of large language models (LLMs) using an external vector cache to store its past states. Like recent vector retrieval approaches, Neurocache uses an efficient k-nearest-neighbor (kNN) algorithm to retrieve relevant past states and incorp... | 2024.naacl-long.50 | 10.18653/v1/2024.naacl-long.50 | null | 2407.02486 | title_snapshot |
2024.naacl-long.51 | Unveiling the Generalization Power of Fine-Tuned Large Language Models | https://aclanthology.org/2024.naacl-long.51/ | [
"Haoran Yang",
"Yumeng Zhang",
"Jiaqi Xu",
"Hongyuan Lu",
"Pheng-Ann Heng",
"Wai Lam"
] | While Large Language Models (LLMs) have demonstrated exceptional multitasking abilities, fine-tuning these models on downstream, domain-specific datasets is often necessary to yield superior performance on test sets compared to their counterparts without fine-tuning. However, the comprehensive effects of fine-tuning on... | 2024.naacl-long.51 | 10.18653/v1/2024.naacl-long.51 | null | 2403.09162 | title_snapshot |
2024.naacl-long.52 | A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning | https://aclanthology.org/2024.naacl-long.52/ | [
"Ruixin Hong",
"Hongming Zhang",
"Xinyu Pang",
"Dong Yu",
"Changshui Zhang"
] | Logical reasoning has been an ongoing pursuit in the field of AI. Despite significant advancements made by large language models (LLMs), they still struggle with complex logical reasoning problems. To enhance reasoning performance, one promising direction is scalable oversight, which requires LLMs to identify their own... | 2024.naacl-long.52 | 10.18653/v1/2024.naacl-long.52 | null | 2311.07954 | title_snapshot |
2024.naacl-long.53 | Exploring Self-supervised Logic-enhanced Training for Large Language Models | https://aclanthology.org/2024.naacl-long.53/ | [
"Fangkai Jiao",
"Zhiyang Teng",
"Bosheng Ding",
"Zhengyuan Liu",
"Nancy Chen",
"Shafiq Joty"
] | Traditional attempts to enhance the logical reasoning abilities of language models often rely on supervised fine-tuning, limiting their generalization to new tasks or domains. Large Language Models (LLMs), with their capacity to condense vast knowledge, can effectively tackle many tasks. Yet, our experiments reveal a g... | 2024.naacl-long.53 | 10.18653/v1/2024.naacl-long.53 | null | 2305.13718 | title_snapshot |
2024.naacl-long.54 | MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning | https://aclanthology.org/2024.naacl-long.54/ | [
"Debrup Das",
"Debopriyo Banerjee",
"Somak Aditya",
"Ashish Kulkarni"
] | Tool-augmented Large Language Models (TALMs) are known to enhance the skillset of large language models (LLMs), thereby, leading to their improved reasoning abilities across many tasks. While, TALMs have been successfully employed in different question-answering benchmarks, their efficacy on complex mathematical reason... | 2024.naacl-long.54 | 10.18653/v1/2024.naacl-long.54 | null | 2402.17231 | title_snapshot |
2024.naacl-long.55 | CoUDA: Coherence Evaluation via Unified Data Augmentation | https://aclanthology.org/2024.naacl-long.55/ | [
"Dawei Zhu",
"Wenhao Wu",
"Yifan Song",
"Fangwei Zhu",
"Ziqiang Cao",
"Sujian Li"
] | Coherence evaluation aims to assess the organization and structure of a discourse, which remains challenging even in the era of large language models. Due to the scarcity of annotated data, data augmentation is commonly used for training coherence evaluation models. However, previous augmentations for this task primari... | 2024.naacl-long.55 | 10.18653/v1/2024.naacl-long.55 | null | 2404.00681 | title_snapshot |
2024.naacl-long.56 | mEdIT: Multilingual Text Editing via Instruction Tuning | https://aclanthology.org/2024.naacl-long.56/ | [
"Vipul Raheja",
"Dimitris Alikaniotis",
"Vivek Kulkarni",
"Bashar Alhafni",
"Dhruv Kumar"
] | We introduce mEdIT, a multi-lingual extension to CoEdIT – the recent state-of-the-art text editing models for writing assistance. mEdIT models are trained by fine-tuning multi-lingual large, pre-trained language models (LLMs) via instruction tuning. They are designed to take instructions from the user specifying the at... | 2024.naacl-long.56 | 10.18653/v1/2024.naacl-long.56 | null | 2402.16472 | title_snapshot |
2024.naacl-long.57 | Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning | https://aclanthology.org/2024.naacl-long.57/ | [
"Yunchao Zhang",
"Zonglin Di",
"Kaiwen Zhou",
"Cihang Xie",
"Xin Wang"
] | Federated embodied agent learning protects the data privacy of individual visual environments by keeping data locally at each client (the individual environment) during training. However, since the local data is inaccessible to the server under federated learning, attackers may easily poison the training data of the lo... | 2024.naacl-long.57 | 10.18653/v1/2024.naacl-long.57 | null | 2211.14769 | title_snapshot |
2024.naacl-long.58 | In-context Learning and Gradient Descent Revisited | https://aclanthology.org/2024.naacl-long.58/ | [
"Gilad Deutch",
"Nadav Magar",
"Tomer Natan",
"Guy Dar"
] | In-context learning (ICL) has shown impressive results in few-shot learning tasks, yet its underlying mechanism is still not fully understood. A recent line of work suggests that ICL performs gradient descent (GD)-based optimization implicitly. While appealing, much of the research focuses on simplified settings, where... | 2024.naacl-long.58 | 10.18653/v1/2024.naacl-long.58 | null | 2311.07772 | title_snapshot |
2024.naacl-long.59 | Corpus Considerations for Annotator Modeling and Scaling | https://aclanthology.org/2024.naacl-long.59/ | [
"Olufunke O. Sarumi",
"Béla Neuendorf",
"Joan Plepi",
"Lucie Flek",
"Jörg Schlötterer",
"Charles Welch"
] | Recent trends in natural language processing research and annotation tasks affirm a paradigm shift from the traditional reliance on a single ground truth to a focus on individual perspectives, particularly in subjective tasks. In scenarios where annotation tasks are meant to encompass diversity, models that solely rely... | 2024.naacl-long.59 | 10.18653/v1/2024.naacl-long.59 | null | 2404.02340 | title_snapshot |
2024.naacl-long.60 | On Large Language Models’ Hallucination with Regard to Known Facts | https://aclanthology.org/2024.naacl-long.60/ | [
"Che Jiang",
"Biqing Qi",
"Xiangyu Hong",
"Dayuan Fu",
"Yang Cheng",
"Fandong Meng",
"Mo Yu",
"Bowen Zhou",
"Jie Zhou"
] | Large language models are successful in answering factoid questions but are also prone to hallucination.We investigate the phenomenon of LLMs possessing correct answer knowledge yet still hallucinating from the perspective of inference dynamics, an area not previously covered in studies on hallucinations.We are able to... | 2024.naacl-long.60 | 10.18653/v1/2024.naacl-long.60 | null | 2403.20009 | title_snapshot |
2024.naacl-long.61 | “One-Size-Fits-All”? Examining Expectations around What Constitute “Fair” or “Good” NLG System Behaviors | https://aclanthology.org/2024.naacl-long.61/ | [
"Li Lucy",
"Su Lin Blodgett",
"Milad Shokouhi",
"Hanna Wallach",
"Alexandra Olteanu"
] | Fairness-related assumptions about what constitute appropriate NLG system behaviors range from invariance, where systems are expected to behave identically for social groups, to adaptation, where behaviors should instead vary across them. To illuminate tensions around invariance and adaptation, we conduct five case stu... | 2024.naacl-long.61 | 10.18653/v1/2024.naacl-long.61 | null | 2310.15398 | title_snapshot |
2024.naacl-long.62 | Language Models Hallucinate, but May Excel at Fact Verification | https://aclanthology.org/2024.naacl-long.62/ | [
"Jian Guan",
"Jesse Dodge",
"David Wadden",
"Minlie Huang",
"Hao Peng"
] | Recent progress in natural language processing (NLP) owes much to remarkable advances in large language models (LLMs). Nevertheless, LLMs frequently “hallucinate,” resulting in non-factual outputs. Our carefully-designed human evaluation substantiates the serious hallucination issue, revealing that even GPT-3.5 produce... | 2024.naacl-long.62 | 10.18653/v1/2024.naacl-long.62 | null | 2310.14564 | title_snapshot |
2024.naacl-long.63 | A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution | https://aclanthology.org/2024.naacl-long.63/ | [
"Bowen Ding",
"Qingkai Min",
"Shengkun Ma",
"Yingjie Li",
"Linyi Yang",
"Yue Zhang"
] | Based on Pre-trained Language Models (PLMs), event coreference resolution (ECR) systems have demonstrated outstanding performance in clustering coreferential events across documents. However, the state-of-the-art system exhibits an excessive reliance on the ‘triggers lexical matching’ spurious pattern in the input ment... | 2024.naacl-long.63 | 10.18653/v1/2024.naacl-long.63 | null | 2404.01921 | title_snapshot |
2024.naacl-long.64 | TrojFSP: Trojan Insertion in Few-shot Prompt Tuning | https://aclanthology.org/2024.naacl-long.64/ | [
"Mengxin Zheng",
"Jiaqi Xue",
"Xun Chen",
"Yanshan Wang",
"Qian Lou",
"Lei Jiang"
] | Prompt tuning is one of the most effective solutions to adapting a fixed pre-trained language model (PLM) for various downstream tasks, especially with only a few input samples. However, the security issues, e.g., Trojan attacks, of prompt tuning on a few data samples are not well-studied. Transferring established data... | 2024.naacl-long.64 | 10.18653/v1/2024.naacl-long.64 | null | 2312.10467 | title_snapshot |
2024.naacl-long.65 | Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models | https://aclanthology.org/2024.naacl-long.65/ | [
"Yi Luo",
"Zhenghao Lin",
"YuHao Zhang",
"Jiashuo Sun",
"Chen Lin",
"Chengjin Xu",
"Xiangdong Su",
"Yelong Shen",
"Jian Guo",
"Yeyun Gong"
] | Large Language Models (LLMs) exhibit impressive capabilities but also present risks such as biased content generation and privacy issues. One of the current alignment techniques includes principle-driven integration, but it faces challenges arising from the imprecision of manually crafted rules and inadequate risk perc... | 2024.naacl-long.65 | 10.18653/v1/2024.naacl-long.65 | null | 2403.11838 | title_snapshot |
2024.naacl-long.66 | X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across Paragraphs | https://aclanthology.org/2024.naacl-long.66/ | [
"Juan Rodriguez",
"Katrin Erk",
"Greg Durrett"
] | Understanding when two pieces of text convey the same information is a goal touching many subproblems in NLP, including textual entailment and fact-checking. This problem becomes more complex when those two pieces of text are in different languages. Here, we introduce X-PARADE (Cross-lingual Paragraph-level Analysis of... | 2024.naacl-long.66 | 10.18653/v1/2024.naacl-long.66 | null | 2309.08873 | title_snapshot |
2024.naacl-long.67 | Topics, Authors, and Institutions in Large Language Model Research: Trends from 17K arXiv Papers | https://aclanthology.org/2024.naacl-long.67/ | [
"Rajiv Movva",
"Sidhika Balachandar",
"Kenny Peng",
"Gabriel Agostini",
"Nikhil Garg",
"Emma Pierson"
] | Large language models (LLMs) are dramatically influencing AI research, spurring discussions on what has changed so far and how to shape the field’s future. To clarify such questions, we analyze a new dataset of 16,979 LLM-related arXiv papers, focusing on recent trends in 2023 vs. 2018-2022. First, we study disciplinar... | 2024.naacl-long.67 | 10.18653/v1/2024.naacl-long.67 | null | 2307.10700 | title_snapshot |
2024.naacl-long.68 | E^5: Zero-shot Hierarchical Table Analysis using Augmented LLMs via Explain, Extract, Execute, Exhibit and Extrapolate | https://aclanthology.org/2024.naacl-long.68/ | [
"Zhehao Zhang",
"Yan Gao",
"Jian-Guang Lou"
] | Analyzing large hierarchical tables with multi-level headers presents challenges due to their complex structure, implicit semantics, and calculation relationships. While recent advancements in large language models (LLMs) have shown promise in flat table analysis, their application to hierarchical tables is constrained... | 2024.naacl-long.68 | 10.18653/v1/2024.naacl-long.68 | null | null | null |
2024.naacl-long.69 | S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Model | https://aclanthology.org/2024.naacl-long.69/ | [
"Fangyu Lei",
"Qian Liu",
"Yiming Huang",
"Shizhu He",
"Jun Zhao",
"Kang Liu"
] | The rapid development of Large Language Models (LLMs) has led to great strides in model capabilities like long-context understanding and reasoning.However, as LLMs are able to process longer contexts, it becomes more challenging to evaluate whether they have acquired certain capabilities, since the length of text (e.g.... | 2024.naacl-long.69 | 10.18653/v1/2024.naacl-long.69 | null | 2310.15147 | title_judge |
2024.naacl-long.70 | MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning | https://aclanthology.org/2024.naacl-long.70/ | [
"Fuxiao Liu",
"Xiaoyang Wang",
"Wenlin Yao",
"Jianshu Chen",
"Kaiqiang Song",
"Sangwoo Cho",
"Yaser Yacoob",
"Dong Yu"
] | With the rapid development of large language models (LLMs) and their integration into large multimodal models (LMMs), there has beenimpressive progress in zero-shot completion of user-oriented vision-language tasks. However, a gap remains in the domain of chartimage understanding due to the distinct abstract components... | 2024.naacl-long.70 | 10.18653/v1/2024.naacl-long.70 | null | 2311.10774 | title_snapshot |
2024.naacl-long.71 | Visual Grounding Helps Learn Word Meanings in Low-Data Regimes | https://aclanthology.org/2024.naacl-long.71/ | [
"Chengxu Zhuang",
"Evelina Fedorenko",
"Jacob Andreas"
] | Modern neural language models (LMs) are powerful tools for modeling human sentence production and comprehension, and their internal representations are remarkably well-aligned with representations of language in the human brain. But to achieve these results, LMs must be trained in distinctly un-human-like ways — requir... | 2024.naacl-long.71 | 10.18653/v1/2024.naacl-long.71 | null | 2310.13257 | title_snapshot |
2024.naacl-long.72 | Accurate Knowledge Distillation via n-best Reranking | https://aclanthology.org/2024.naacl-long.72/ | [
"Hendra Setiawan"
] | We propose utilizing n-best reranking to enhance Sequence-Level Knowledge Distillation (Kim and Rush, 2016) where we extract pseudo-labels for student model’s training data from top n-best hypotheses and leverage a diverse set of models with different inductive biases, objective functions or architectures, including so... | 2024.naacl-long.72 | 10.18653/v1/2024.naacl-long.72 | null | 2305.12057 | title_judge |
2024.naacl-long.73 | AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition | https://aclanthology.org/2024.naacl-long.73/ | [
"Zhaorun Chen",
"Zhuokai Zhao",
"Zhihong Zhu",
"Ruiqi Zhang",
"Xiang Li",
"Bhiksha Raj",
"Huaxiu Yao"
] | Recent advancements in large language models (LLMs) have shown promise in multi-step reasoning tasks, yet their reliance on extensive manual labeling to provide procedural feedback remains a significant impediment. To address this challenge, in this paper, we propose a novel self-supervised framework **AutoPRM** that e... | 2024.naacl-long.73 | 10.18653/v1/2024.naacl-long.73 | null | 2402.11452 | title_snapshot |
2024.naacl-long.74 | SEMQA: Semi-Extractive Multi-Source Question Answering | https://aclanthology.org/2024.naacl-long.74/ | [
"Tal Schuster",
"Adam Lelkes",
"Haitian Sun",
"Jai Gupta",
"Jonathan Berant",
"William Cohen",
"Donald Metzler"
] | Recently proposed long-form question answering (QA) systems, supported by large language models (LLMs), have shown promising capabilities. Yet, attributing and verifying their generated abstractive answers can be difficult, and automatically evaluating their accuracy remains an ongoing challenge.In this work, we introd... | 2024.naacl-long.74 | 10.18653/v1/2024.naacl-long.74 | null | 2311.04886 | title_snapshot |
2024.naacl-long.75 | Fine-Tuning Language Models with Reward Learning on Policy | https://aclanthology.org/2024.naacl-long.75/ | [
"Hao Lang",
"Fei Huang",
"Yongbin Li"
] | Reinforcement learning from human feedback (RLHF) has emerged as an effective approach to aligning large language models (LLMs) to human preferences.RLHF contains three steps, i.e., human preference collecting, reward learning, and policy optimization, which are usually performed serially.Despite its popularity, howeve... | 2024.naacl-long.75 | 10.18653/v1/2024.naacl-long.75 | null | 2403.19279 | title_snapshot |
2024.naacl-long.76 | A Universal Dependencies Treebank for Highland Puebla Nahuatl | https://aclanthology.org/2024.naacl-long.76/ | [
"Robert Pugh",
"Francis Tyers"
] | We present a Universal Dependencies (UD) treebank for Highland Puebla Nahuatl. The treebank is only the second such UD corpus for a Mexican language, and supplements an existing treebank for another Nahuatl variant. We describe the process of data collection, annotation decisions and interesting syntactic constructions... | 2024.naacl-long.76 | 10.18653/v1/2024.naacl-long.76 | null | null | null |
2024.naacl-long.77 | COPAL-ID: Indonesian Language Reasoning with Local Culture and Nuances | https://aclanthology.org/2024.naacl-long.77/ | [
"Haryo Wibowo",
"Erland Fuadi",
"Made Nityasya",
"Radityo Eko Prasojo",
"Alham Aji"
] | We present COPAL-ID, a novel, public Indonesian language common sense reasoning dataset. Unlike the previous Indonesian COPA dataset (XCOPA-ID), COPAL-ID incorporates Indonesian local and cultural nuances, and therefore, provides a more natural portrayal of day-to-day causal reasoning within the Indonesian cultural sph... | 2024.naacl-long.77 | 10.18653/v1/2024.naacl-long.77 | null | 2311.01012 | title_snapshot |
2024.naacl-long.78 | IterAlign: Iterative Constitutional Alignment of Large Language Models | https://aclanthology.org/2024.naacl-long.78/ | [
"Xiusi Chen",
"Hongzhi Wen",
"Sreyashi Nag",
"Chen Luo",
"Qingyu Yin",
"Ruirui Li",
"Zheng Li",
"Wei Wang"
] | With the rapid development of large language models (LLMs), aligning LLMs with human values and societal norms to ensure their reliability and safety has become crucial. Reinforcement learning with human feedback (RLHF) and Constitutional AI (CAI) have been proposed for LLM alignment. However, these methods require eit... | 2024.naacl-long.78 | 10.18653/v1/2024.naacl-long.78 | null | 2403.18341 | title_snapshot |
2024.naacl-long.79 | OrchestraLLM: Efficient Orchestration of Language Models for Dialogue State Tracking | https://aclanthology.org/2024.naacl-long.79/ | [
"Chia-Hsuan Lee",
"Hao Cheng",
"Mari Ostendorf"
] | Large language models (LLMs) have revolutionized the landscape of Natural Language Processing, but are computationally expensive. To reduce the cost without sacrificing performance, previous studies have explored various approaches to harness the potential of Smaller Language Models (SLMs) as cost-effective alternative... | 2024.naacl-long.79 | 10.18653/v1/2024.naacl-long.79 | null | 2311.09758 | title_snapshot |
2024.naacl-long.80 | Multi-Operational Mathematical Derivations in Latent Space | https://aclanthology.org/2024.naacl-long.80/ | [
"Marco Valentino",
"Jordan Meadows",
"Lan Zhang",
"Andre Freitas"
] | This paper investigates the possibility of approximating multiple mathematical operations in latent space for expression derivation. To this end, we introduce different multi-operational representation paradigms, modelling mathematical operations as explicit geometric transformations. By leveraging a symbolic engine, w... | 2024.naacl-long.80 | 10.18653/v1/2024.naacl-long.80 | null | 2311.01230 | title_snapshot |
2024.naacl-long.81 | Large Language Models Help Humans Verify Truthfulness – Except When They Are Convincingly Wrong | https://aclanthology.org/2024.naacl-long.81/ | [
"Chenglei Si",
"Navita Goyal",
"Tongshuang Wu",
"Chen Zhao",
"Shi Feng",
"Hal Daumé III",
"Jordan Boyd-Graber"
] | Large Language Models (LLMs) are increasingly used for accessing information on the web. Their truthfulness and factuality are thus of great interest. To help users make the right decisions about the information they get, LLMs should not only provide information but also help users fact-check it. We conduct human exper... | 2024.naacl-long.81 | 10.18653/v1/2024.naacl-long.81 | null | 2310.12558 | title_snapshot |
2024.naacl-long.82 | XferBench: a Data-Driven Benchmark for Emergent Language | https://aclanthology.org/2024.naacl-long.82/ | [
"Brendon Boldt",
"David Mortensen"
] | In this paper, we introduce a benchmark for evaluating the overall quality of emergent languages using data-driven methods. Specifically, we interpret the notion of the “quality” of an emergent language as its similarity to human language within a deep learning framework. We measure this by using the emergent language ... | 2024.naacl-long.82 | 10.18653/v1/2024.naacl-long.82 | null | 2407.03456 | title_snapshot |
2024.naacl-long.83 | Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation | https://aclanthology.org/2024.naacl-long.83/ | [
"Se-eun Yoon",
"Zhankui He",
"Jessica Echterhoff",
"Julian McAuley"
] | Synthetic users are cost-effective proxies for real users in the evaluation of conversational recommender systems. Large language models show promise in simulating human-like behavior, raising the question of their ability to represent a diverse population of users. We introduce a new protocol to measure the degree to ... | 2024.naacl-long.83 | 10.18653/v1/2024.naacl-long.83 | null | 2403.09738 | title_snapshot |
2024.naacl-long.84 | A Symbolic Framework for Evaluating Mathematical Reasoning and Generalisation with Transformers | https://aclanthology.org/2024.naacl-long.84/ | [
"Jordan Meadows",
"Marco Valentino",
"Damien Teney",
"Andre Freitas"
] | This paper proposes a methodology for generating and perturbing detailed derivations of equations at scale, aided by a symbolic engine, to evaluate the generalisability of Transformers to out-of-distribution mathematical reasoning problems. Instantiating the framework in the context of sequence classification tasks, we... | 2024.naacl-long.84 | 10.18653/v1/2024.naacl-long.84 | null | 2305.12563 | title_snapshot |
2024.naacl-long.85 | Identifying Linear Relational Concepts in Large Language Models | https://aclanthology.org/2024.naacl-long.85/ | [
"David Chanin",
"Anthony Hunter",
"Oana-Maria Camburu"
] | Transformer language models (LMs) have been shown to represent concepts as directions in the latent space of hidden activations. However, for any human-interpretable concept, how can we find its direction in the latent space? We present a technique called linear relational concepts (LRC) for finding concept directions ... | 2024.naacl-long.85 | 10.18653/v1/2024.naacl-long.85 | null | 2311.08968 | title_snapshot |
2024.naacl-long.86 | Benchmark Transparency: Measuring the Impact of Data on Evaluation | https://aclanthology.org/2024.naacl-long.86/ | [
"Venelin Kovatchev",
"Matthew Lease"
] | In this paper we present an exploratory research on quantifying the impact that data distribution has on the performance and evaluation of NLP models. We propose an automated framework that measures the data point distribution across 6 different dimensions: ambiguity, difficulty, discriminability, length, noise, and pe... | 2024.naacl-long.86 | 10.18653/v1/2024.naacl-long.86 | null | 2404.00748 | title_snapshot |
2024.naacl-long.87 | JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models | https://aclanthology.org/2024.naacl-long.87/ | [
"Jillian Fisher",
"Ximing Lu",
"Jaehun Jung",
"Liwei Jiang",
"Zaid Harchaoui",
"Yejin Choi"
] | The permanence of online content combined with the enhanced authorship identification techniques calls for stronger computational methods to protect the identity and privacy of online authorship when needed, e.g., blind reviews for scientific papers, anonymous online reviews, or anonymous interactions in the mental hea... | 2024.naacl-long.87 | 10.18653/v1/2024.naacl-long.87 | null | 2402.08761 | title_snapshot |
2024.naacl-long.88 | REST: Retrieval-Based Speculative Decoding | https://aclanthology.org/2024.naacl-long.88/ | [
"Zhenyu He",
"Zexuan Zhong",
"Tianle Cai",
"Jason Lee",
"Di He"
] | We introduce Retrieval-Based Speculative Decoding (REST), a novel algorithm designed to speed up language model generation. The key insight driving the development of REST is the observation that the process of text generation often includes certain common phases and patterns. Unlike previous methods that rely on a dra... | 2024.naacl-long.88 | 10.18653/v1/2024.naacl-long.88 | null | 2311.08252 | title_snapshot |
2024.naacl-long.89 | Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations | https://aclanthology.org/2024.naacl-long.89/ | [
"Sihao Chen",
"Hongming Zhang",
"Tong Chen",
"Ben Zhou",
"Wenhao Yu",
"Dian Yu",
"Baolin Peng",
"Hongwei Wang",
"Dan Roth",
"Dong Yu"
] | We introduce sub-sentence encoder, a contrastively-learned contextual embedding model for fine-grained semantic representation of text. In contrast to the standard practice with sentence embeddings, where the meaning of an entire sequence of text is encoded into a fixed-length vector, the sub-sentence encoder learns to... | 2024.naacl-long.89 | 10.18653/v1/2024.naacl-long.89 | null | 2311.04335 | title_snapshot |
2024.naacl-long.90 | MSciNLI: A Diverse Benchmark for Scientific Natural Language Inference | https://aclanthology.org/2024.naacl-long.90/ | [
"Mobashir Sadat",
"Cornelia Caragea"
] | The task of scientific Natural Language Inference (NLI) involves predicting the semantic relation between two sentences extracted from research articles. This task was recently proposed along with a new dataset called SciNLI derived from papers published in the computational linguistics domain. In this paper, we aim to... | 2024.naacl-long.90 | 10.18653/v1/2024.naacl-long.90 | null | 2404.08066 | title_snapshot |
2024.naacl-long.91 | Causal Inference for Human-Language Model Collaboration | https://aclanthology.org/2024.naacl-long.91/ | [
"Bohan Zhang",
"Yixin Wang",
"Paramveer Dhillon"
] | In this paper, we examine the collaborative dynamics between humansand language models (LMs), where the interactions typically involveLMs proposing text segments and humans editing or responding to theseproposals. Productive engagement with LMs in such scenarios necessitates that humans discern effective text-based int... | 2024.naacl-long.91 | 10.18653/v1/2024.naacl-long.91 | null | 2404.00207 | title_snapshot |
2024.naacl-long.92 | SELF-GUARD: Empower the LLM to Safeguard Itself | https://aclanthology.org/2024.naacl-long.92/ | [
"Zezhong Wang",
"Fangkai Yang",
"Lu Wang",
"Pu Zhao",
"Hongru Wang",
"Liang Chen",
"Qingwei Lin",
"Kam-Fai Wong"
] | With the increasing risk posed by jailbreak attacks, recent studies have investigated various methods to improve the safety of large language models (LLMs), mainly falling into two strategies: safety training and safeguards. Safety training involves fine-tuning the LLM with adversarial samples, which activate the LLM’s... | 2024.naacl-long.92 | 10.18653/v1/2024.naacl-long.92 | null | 2310.15851 | title_snapshot |
2024.naacl-long.93 | COSIGN: Contextual Facts Guided Generation for Knowledge Graph Completion | https://aclanthology.org/2024.naacl-long.93/ | [
"Jinpeng Li",
"Hang Yu",
"Xiangfeng Luo",
"Qian Liu"
] | Knowledge graph completion (KGC) aims to infer missing facts based on existing facts within a KG. Recently, research on generative models (GMs) has addressed the limitations of embedding methods in terms of generality and scalability. However, GM-based methods are sensitive to contextual facts on KG, so the contextual ... | 2024.naacl-long.93 | 10.18653/v1/2024.naacl-long.93 | null | null | null |
2024.naacl-long.94 | Toward Informal Language Processing: Knowledge of Slang in Large Language Models | https://aclanthology.org/2024.naacl-long.94/ | [
"Zhewei Sun",
"Qian Hu",
"Rahul Gupta",
"Richard Zemel",
"Yang Xu"
] | Recent advancement in large language models (LLMs) has offered a strong potential for natural language systems to process informal language. A representative form of informal language is slang, used commonly in daily conversations and online social media. To date, slang has not been comprehensively evaluated in LLMs du... | 2024.naacl-long.94 | 10.18653/v1/2024.naacl-long.94 | null | 2404.02323 | title_snapshot |
2024.naacl-long.95 | Ghostbuster: Detecting Text Ghostwritten by Large Language Models | https://aclanthology.org/2024.naacl-long.95/ | [
"Vivek Verma",
"Eve Fleisig",
"Nicholas Tomlin",
"Dan Klein"
] | We introduce Ghostbuster, a state-of-the-art system for detecting AI-generated text.Our method works by passing documents through a series of weaker language models, running a structured search over possible combinations of their features, and then training a classifier on the selected features to predict whether docum... | 2024.naacl-long.95 | 10.18653/v1/2024.naacl-long.95 | null | 2305.15047 | title_snapshot |
2024.naacl-long.96 | End-to-End Beam Retrieval for Multi-Hop Question Answering | https://aclanthology.org/2024.naacl-long.96/ | [
"Jiahao Zhang",
"Haiyang Zhang",
"Dongmei Zhang",
"Liu Yong",
"Shen Huang"
] | Multi-hop question answering (QA) involves finding multiple relevant passages and step-by-step reasoning to answer complex questions, indicating a retrieve-and-read paradigm. However, previous retrievers were customized for two-hop questions, and most of them were trained separately across different hops, resulting in ... | 2024.naacl-long.96 | 10.18653/v1/2024.naacl-long.96 | null | 2308.08973 | title_snapshot |
2024.naacl-long.97 | Leveraging Generative Large Language Models with Visual Instruction and Demonstration Retrieval for Multimodal Sarcasm Detection | https://aclanthology.org/2024.naacl-long.97/ | [
"Binghao Tang",
"Boda Lin",
"Haolong Yan",
"Si Li"
] | Multimodal sarcasm detection aims to identify sarcasm in the given image-text pairs and has wide applications in the multimodal domains. Previous works primarily design complex network structures to fuse the image-text modality features for classification. However, such complicated structures may risk overfitting on in... | 2024.naacl-long.97 | 10.18653/v1/2024.naacl-long.97 | null | null | null |
2024.naacl-long.98 | Multi-Scale Prompt Memory-Augmented Model for Black-Box Scenarios | https://aclanthology.org/2024.naacl-long.98/ | [
"Xiaojun Kuang",
"C. L. Philip Chen",
"Shuzhen Li",
"Tong Zhang"
] | Black-box few-shot text classification handles text classification in limited data without accessing the parameters and gradients of language models (LMs). Existing black-box optimization methods have demonstrated strong few-shot learning capabilities. However, they still require numerous LMs’ calls to search optimal p... | 2024.naacl-long.98 | 10.18653/v1/2024.naacl-long.98 | null | null | null |
2024.naacl-long.99 | Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction | https://aclanthology.org/2024.naacl-long.99/ | [
"Chenming Tang",
"Fanyi Qu",
"Yunfang Wu"
] | In the era of large language models (LLMs), in-context learning (ICL) stands out as an effective prompting strategy that explores LLMs’ potency across various tasks. However, applying LLMs to grammatical error correction (GEC) is still a challenging task. In this paper, we propose a novel ungrammatical-syntax-based in-... | 2024.naacl-long.99 | 10.18653/v1/2024.naacl-long.99 | null | 2403.19283 | title_snapshot |
2024.naacl-long.100 | BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer | https://aclanthology.org/2024.naacl-long.100/ | [
"Akari Asai",
"Sneha Kudugunta",
"Xinyan Yu",
"Terra Blevins",
"Hila Gonen",
"Machel Reid",
"Yulia Tsvetkov",
"Sebastian Ruder",
"Hannaneh Hajishirzi"
] | Despite remarkable advancements in few-shot generalization in natural language processing, most models are developed and evaluated primarily in English. To establish a rigorous and equitable evaluation framework for few-shot cross-lingual transfer, we introduce a new benchmark, called BUFFET, which unifies 15 diverse t... | 2024.naacl-long.100 | 10.18653/v1/2024.naacl-long.100 | null | 2305.14857 | title_snapshot |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- -