bibtex_url stringlengths 41 50 | bibtext stringlengths 693 2.88k | abstract stringlengths 0 2k | authors listlengths 1 45 | title stringlengths 21 206 | id stringlengths 7 16 | type stringclasses 2
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|---|---|---|---|---|---|---|---|
https://aclanthology.org/2024.findings-acl.368.bib | @inproceedings{cui-etal-2024-unveiling,
title = "Unveiling the Art of Heading Design: A Harmonious Blend of Summarization, Neology, and Algorithm",
author = "Cui, Shaobo and
Feng, Yiyang and
Mao, Yisong and
Hou, Yifan and
Faltings, Boi",
editor = "Ku, Lun-Wei and
Martins, ... | Crafting an appealing heading is crucial for attracting readers and marketing work or products. A popular way is to summarize the main idea with a refined description and a memorable acronym. However, there lacks a systematic study and a formal benchmark including datasets and metrics. Motivated by this absence, we int... | [
"Cui, Shaobo",
"Feng, Yiyang",
"Mao, Yisong",
"Hou, Yifan",
"Faltings, Boi"
] | Unveiling the Art of Heading Design: A Harmonious Blend of Summarization, Neology, and Algorithm | findings-acl.368 | Poster | 2006.03743v1 |
https://aclanthology.org/2024.findings-acl.369.bib | @inproceedings{wuehrl-etal-2024-understanding,
title = "Understanding Fine-grained Distortions in Reports of Scientific Findings",
author = "Wuehrl, Amelie and
Wright, Dustin and
Klinger, Roman and
Augenstein, Isabelle",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikuma... | Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions. Given the rapidly increasing volume of science communication in recent years, a fine-grained understanding of how findings from scientific publications are reported to... | [
"Wuehrl, Amelie",
"Wright, Dustin",
"Klinger, Roman",
"Augenstein, Isabelle"
] | Understanding Fine-grained Distortions in Reports of Scientific Findings | findings-acl.369 | Poster | 2402.12431v1 |
https://aclanthology.org/2024.findings-acl.370.bib | @inproceedings{jin-etal-2024-mm,
title = "{MM}-{SOC}: Benchmarking Multimodal Large Language Models in Social Media Platforms",
author = "Jin, Yiqiao and
Choi, Minje and
Verma, Gaurav and
Wang, Jindong and
Kumar, Srijan",
editor = "Ku, Lun-Wei and
Martins, Andre and
... | Social media platforms are hubs for multimodal information exchange, encompassing text, images, and videos, making it challenging for machines to comprehend the information or emotions associated with interactions in online spaces. Multimodal Large Language Models (MLLMs) have emerged as a promising solution to address... | [
"Jin, Yiqiao",
"Choi, Minje",
"Verma, Gaurav",
"Wang, Jindong",
"Kumar, Srijan"
] | {MM}-{SOC}: Benchmarking Multimodal Large Language Models in Social Media Platforms | findings-acl.370 | Poster | 2402.14154v2 |
https://aclanthology.org/2024.findings-acl.371.bib | @inproceedings{srivastava-etal-2024-instances,
title = "Instances Need More Care: Rewriting Prompts for Instances with {LLM}s in the Loop Yields Better Zero-Shot Performance",
author = "Srivastava, Saurabh and
Huang, Chengyue and
Fan, Weiguo and
Yao, Ziyu",
editor = "Ku, Lun-Wei and
... | Large language models (LLMs) have revolutionized zero-shot task performance, mitigating the need for task-specific annotations while enhancing task generalizability. Despite its advancements, current methods using trigger phrases such as {``}Let{'}s think step by step{''} remain limited. This study introduces PRomPTed,... | [
"Srivastava, Saurabh",
"Huang, Chengyue",
"Fan, Weiguo",
"Yao, Ziyu"
] | Instances Need More Care: Rewriting Prompts for Instances with {LLM}s in the Loop Yields Better Zero-Shot Performance | findings-acl.371 | Poster | 2310.02107v4 |
https://aclanthology.org/2024.findings-acl.372.bib | @inproceedings{xiong-etal-2024-benchmarking,
title = "Benchmarking Retrieval-Augmented Generation for Medicine",
author = "Xiong, Guangzhi and
Jin, Qiao and
Lu, Zhiyong and
Zhang, Aidong",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Find... | While large language models (LLMs) have achieved state-of-the-art performance on a wide range of medical question answering (QA) tasks, they still face challenges with hallucinations and outdated knowledge. Retrieval-augmented generation (RAG) is a promising solution and has been widely adopted. However, a RAG system c... | [
"Xiong, Guangzhi",
"Jin, Qiao",
"Lu, Zhiyong",
"Zhang, Aidong"
] | Benchmarking Retrieval-Augmented Generation for Medicine | findings-acl.372 | Poster | 2311.09774v1 |
https://aclanthology.org/2024.findings-acl.373.bib | @inproceedings{yuan-etal-2024-chatmusician,
title = "{C}hat{M}usician: Understanding and Generating Music Intrinsically with {LLM}",
author = "Yuan, Ruibin and
Lin, Hanfeng and
Wang, Yi and
Tian, Zeyue and
Wu, Shangda and
Shen, Tianhao and
Zhang, Ge and
Wu, Yuhan... | While LLMs demonstrate impressive capabilities in musical knowledge, we find that music reasoning is still an unsolved task.We introduce ChatMusician, an open-source large language model (LLM) that integrates intrinsic musical abilities. It is based on continual pre-training and finetuning LLaMA2 on a text-compatible m... | [
"Yuan, Ruibin",
"Lin, Hanfeng",
"Wang, Yi",
"Tian, Zeyue",
"Wu, Shangda",
"Shen, Tianhao",
"Zhang, Ge",
"Wu, Yuhang",
"Liu, Cong",
"Zhou, Ziya",
"Xue, Liumeng",
"Ma, Ziyang",
"Liu, Qin",
"Zheng, Tianyu",
"Li, Yizhi",
"Ma, Yinghao",
"Liang, Yiming",
"Chi, Xiaowei",
"Liu, Ruibo",
... | {C}hat{M}usician: Understanding and Generating Music Intrinsically with {LLM} | findings-acl.373 | Poster | 2407.21531v1 |
https://aclanthology.org/2024.findings-acl.374.bib | @inproceedings{tan-etal-2024-towards,
title = "Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop {QA} Dataset and Pseudo-Instruction Tuning",
author = "Tan, Qingyu and
Ng, Hwee Tou and
Bing, Lidong",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, ... | Knowledge in the real world is being updated constantly. However, it is costly to frequently update large language models (LLMs). Therefore, it is crucial for LLMs to understand the concept of temporal knowledge. However, prior works on temporal question answering (TQA) did not emphasize multi-answer and multi-hop type... | [
"Tan, Qingyu",
"Ng, Hwee Tou",
"Bing, Lidong"
] | Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop {QA} Dataset and Pseudo-Instruction Tuning | findings-acl.374 | Poster | 2311.09821v2 |
https://aclanthology.org/2024.findings-acl.375.bib | @inproceedings{voronov-etal-2024-mind,
title = "Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements",
author = "Voronov, Anton and
Wolf, Lena and
Ryabinin, Max",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findin... | Large language models demonstrate a remarkable capability for learning to solve new tasks from a few examples.The $\textit{prompt template}$, or the way the input examples are formatted to obtain the prompt, is an important yet often overlooked aspect of in-context learning.In this work, we conduct a comprehensive stud... | [
"Voronov, Anton",
"Wolf, Lena",
"Ryabinin, Max"
] | Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements | findings-acl.375 | Poster | 2401.06766v3 |
https://aclanthology.org/2024.findings-acl.376.bib | @inproceedings{liu-etal-2024-knowledge-graph,
title = "Knowledge Graph-Enhanced Large Language Models via Path Selection",
author = "Liu, Haochen and
Wang, Song and
Zhu, Yaochen and
Dong, Yushun and
Li, Jundong",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar... | Large Language Models (LLMs) have shown unprecedented performance in various real-world applications. However, they are known to generate factually inaccurate outputs, a.k.a. the hallucination problem. In recent years, incorporating external knowledge extracted from Knowledge Graphs (KGs) has become a promising strateg... | [
"Liu, Haochen",
"Wang, Song",
"Zhu, Yaochen",
"Dong, Yushun",
"Li, Jundong"
] | Knowledge Graph-Enhanced Large Language Models via Path Selection | findings-acl.376 | Poster | 2406.13862v1 |
https://aclanthology.org/2024.findings-acl.377.bib | @inproceedings{huang-etal-2024-ottawa,
title = "{OTTAWA}: Optimal {T}ranspor{T} Adaptive Word Aligner for Hallucination and Omission Translation Errors Detection",
author = "Huang, Chenyang and
Ghaddar, Abbas and
Kobyzev, Ivan and
Rezagholizadeh, Mehdi and
Zaiane, Osmar and
Ch... | Recently, there has been considerable attention on detecting hallucinations and omissions in Machine Translation (MT) systems. The two dominant approaches to tackle this task involve analyzing the MT system{'}s internal states or relying on the output of external tools, such as sentence similarity or MT quality estimat... | [
"Huang, Chenyang",
"Ghaddar, Abbas",
"Kobyzev, Ivan",
"Rezagholizadeh, Mehdi",
"Zaiane, Osmar",
"Chen, Boxing"
] | {OTTAWA}: Optimal {T}ranspor{T} Adaptive Word Aligner for Hallucination and Omission Translation Errors Detection | findings-acl.377 | Poster | 2406.01919v1 |
https://aclanthology.org/2024.findings-acl.378.bib | @inproceedings{yu-etal-2024-onsep,
title = "{ONSEP}: A Novel Online Neural-Symbolic Framework for Event Prediction Based on Large Language Model",
author = "Yu, Xuanqing and
Sun, Wangtao and
Li, Jingwei and
Liu, Kang and
Liu, Chengbao and
Tan, Jie",
editor = "Ku, Lun-Wei ... | In the realm of event prediction, temporal knowledge graph forecasting (TKGF) stands as a pivotal technique. Previous approaches face the challenges of not utilizing experience during testing and relying on a single short-term history, which limits adaptation to evolving data. In this paper, we introduce the Online Neu... | [
"Yu, Xuanqing",
"Sun, Wangtao",
"Li, Jingwei",
"Liu, Kang",
"Liu, Chengbao",
"Tan, Jie"
] | {ONSEP}: A Novel Online Neural-Symbolic Framework for Event Prediction Based on Large Language Model | findings-acl.378 | Poster | 2311.17351v1 |
https://aclanthology.org/2024.findings-acl.379.bib | @inproceedings{sun-etal-2024-speech,
title = "Speech-based Slot Filling using Large Language Models",
author = "Sun, Guangzhi and
Feng, Shutong and
Jiang, Dongcheng and
Zhang, Chao and
Gasic, Milica and
Woodland, Phil",
editor = "Ku, Lun-Wei and
Martins, Andre and
... | Recently, advancements in large language models (LLMs) have shown an unprecedented ability across various language tasks. This paper investigates the potential application of LLMs to slot filling with noisy ASR transcriptions, via both in-context learning and task-specific fine-tuning. Dedicated prompt designs and nois... | [
"Sun, Guangzhi",
"Feng, Shutong",
"Jiang, Dongcheng",
"Zhang, Chao",
"Gasic, Milica",
"Woodl",
", Phil"
] | Speech-based Slot Filling using Large Language Models | findings-acl.379 | Poster | 1811.01331v2 |
https://aclanthology.org/2024.findings-acl.380.bib | @inproceedings{li-etal-2024-big,
title = "Too Big to Fail: Larger Language Models are Disproportionately Resilient to Induction of Dementia-Related Linguistic Anomalies",
author = "Li, Changye and
Sheng, Zhecheng and
Cohen, Trevor and
Pakhomov, Serguei",
editor = "Ku, Lun-Wei and
... | As artificial neural networks grow in complexity, understanding their inner workings becomes increasingly challenging, which is particularly important in healthcare applications. The intrinsic evaluation metrics of autoregressive neural language models (NLMs), perplexity (PPL), can reflect how {``}surprised{''} an NLM ... | [
"Li, Changye",
"Sheng, Zhecheng",
"Cohen, Trevor",
"Pakhomov, Serguei"
] | Too Big to Fail: Larger Language Models are Disproportionately Resilient to Induction of Dementia-Related Linguistic Anomalies | findings-acl.380 | Poster | 2406.02830v1 |
https://aclanthology.org/2024.findings-acl.381.bib | @inproceedings{paz-argaman-etal-2024-hesum,
title = "{H}e{S}um: a Novel Dataset for Abstractive Text Summarization in {H}ebrew",
author = "Paz-Argaman, Tzuf and
Mondshine, Itai and
Achi Mordechai, Asaf and
Tsarfaty, Reut",
editor = "Ku, Lun-Wei and
Martins, Andre and
Sriku... | While large language models (LLMs) excel in various natural language tasks in English, their performance in low-resource languages like Hebrew, especially for generative tasks such as abstractive summarization, remains unclear. The high morphological richness in Hebrew adds further challenges due to the ambiguity in se... | [
"Paz-Argaman, Tzuf",
"Mondshine, Itai",
"Achi Mordechai, Asaf",
"Tsarfaty, Reut"
] | {H}e{S}um: a Novel Dataset for Abstractive Text Summarization in {H}ebrew | findings-acl.381 | Poster | 2406.03897v2 |
https://aclanthology.org/2024.findings-acl.382.bib | @inproceedings{wang-zhao-2024-tram,
title = "{TRAM}: Benchmarking Temporal Reasoning for Large Language Models",
author = "Wang, Yuqing and
Zhao, Yun",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguisti... | Reasoning about time is essential for understanding the nuances of events described in natural language. Previous research on this topic has been limited in scope, characterized by a lack of standardized benchmarks that would allow for consistent evaluations across different studies. In this paper, we introduce TRAM, a... | [
"Wang, Yuqing",
"Zhao, Yun"
] | {TRAM}: Benchmarking Temporal Reasoning for Large Language Models | findings-acl.382 | Poster | 2406.09072v1 |
https://aclanthology.org/2024.findings-acl.383.bib | @inproceedings{amayuelas-etal-2024-knowledge,
title = "Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models",
author = "Amayuelas, Alfonso and
Wong, Kyle and
Pan, Liangming and
Chen, Wenhu and
Wang, William Yang",
editor = "Ku, Lun-Wei and
... | This paper investigates the capabilities of Large Language Models (LLMs) in understanding their knowledge and uncertainty over questions. Specifically, we focus on addressing known-unknown questions, characterized by high uncertainty due to the absence of definitive answers. To facilitate our study, we collect a new da... | [
"Amayuelas, Alfonso",
"Wong, Kyle",
"Pan, Liangming",
"Chen, Wenhu",
"Wang, William Yang"
] | Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models | findings-acl.383 | Poster | 2003.10775v2 |
https://aclanthology.org/2024.findings-acl.384.bib | @inproceedings{cui-etal-2024-exploring,
title = "Exploring Defeasibility in Causal Reasoning",
author = "Cui, Shaobo and
Milikic, Lazar and
Feng, Yiyang and
Ismayilzada, Mete and
Paul, Debjit and
Bosselut, Antoine and
Faltings, Boi",
editor = "Ku, Lun-Wei and
... | Defeasibility in causal reasoning implies that the causal relationship between cause and effect can be strengthened or weakened. Namely, the causal strength between cause and effect should increase or decrease with the incorporation of strengthening arguments (supporters) or weakening arguments (defeaters), respectivel... | [
"Cui, Shaobo",
"Milikic, Lazar",
"Feng, Yiyang",
"Ismayilzada, Mete",
"Paul, Debjit",
"Bosselut, Antoine",
"Faltings, Boi"
] | Exploring Defeasibility in Causal Reasoning | findings-acl.384 | Poster | 2401.03183v2 |
https://aclanthology.org/2024.findings-acl.385.bib | @inproceedings{gandhi-etal-2024-better,
title = "Better Synthetic Data by Retrieving and Transforming Existing Datasets",
author = "Gandhi, Saumya and
Gala, Ritu and
Viswanathan, Vijay and
Wu, Tongshuang and
Neubig, Graham",
editor = "Ku, Lun-Wei and
Martins, Andre and
... | Despite recent advances in large language models, building dependable and deployable NLP models typically requires abundant, high-quality training data. However, task-specific data is not available for many use cases, and manually curating task-specific data is labor-intensive. Recent work has studied prompt-driven syn... | [
"G",
"hi, Saumya",
"Gala, Ritu",
"Viswanathan, Vijay",
"Wu, Tongshuang",
"Neubig, Graham"
] | Better Synthetic Data by Retrieving and Transforming Existing Datasets | findings-acl.385 | Poster | 2404.14361v3 |
https://aclanthology.org/2024.findings-acl.386.bib | @inproceedings{xiang-etal-2024-addressing,
title = "Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models",
author = "Xiang, Yanzheng and
Yan, Hanqi and
Gui, Lin and
He, Yulan",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vi... | In-context learning has become a popular paradigm in natural language processing. However, its performance can be significantly influenced by the order of in-context demonstration examples. In this paper, we found that causal language models (CausalLMs) are more sensitive to this order compared to prefix language model... | [
"Xiang, Yanzheng",
"Yan, Hanqi",
"Gui, Lin",
"He, Yulan"
] | Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models | findings-acl.386 | Poster | 2402.15637v2 |
https://aclanthology.org/2024.findings-acl.387.bib | @inproceedings{plepi-etal-2024-perspective,
title = "Perspective Taking through Generating Responses to Conflict Situations",
author = "Plepi, Joan and
Welch, Charles and
Flek, Lucie",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the... | Although language model performance across diverse tasks continues to improve, these models still struggle to understand and explain the beliefs of other people. This skill requires perspective-taking, the process of conceptualizing the point of view of another person. Perspective taking becomes challenging when the te... | [
"Plepi, Joan",
"Welch, Charles",
"Flek, Lucie"
] | Perspective Taking through Generating Responses to Conflict Situations | findings-acl.387 | Poster | 2310.00935v1 |
https://aclanthology.org/2024.findings-acl.388.bib | @inproceedings{lee-etal-2024-llm2llm,
title = "{LLM}2{LLM}: Boosting {LLM}s with Novel Iterative Data Enhancement",
author = "Lee, Nicholas and
Wattanawong, Thanakul and
Kim, Sehoon and
Mangalam, Karttikeya and
Shen, Sheng and
Anumanchipalli, Gopala and
Mahoney, Michael... | Pretrained large language models (LLMs) are currently state-of-the-art for solving the vast majority of natural language processing tasks. While many real-world applications still require fine-tuning to reach satisfactory levels of performance, many of them are in the low-data regime, making fine-tuning challenging. To... | [
"Lee, Nicholas",
"Wattanawong, Thanakul",
"Kim, Sehoon",
"Mangalam, Karttikeya",
"Shen, Sheng",
"Anumanchipalli, Gopala",
"Mahoney, Michael",
"Keutzer, Kurt",
"Gholami, Amir"
] | {LLM}2{LLM}: Boosting {LLM}s with Novel Iterative Data Enhancement | findings-acl.388 | Poster | 2402.12146v3 |
https://aclanthology.org/2024.findings-acl.389.bib | @inproceedings{ernst-etal-2024-power,
title = "The Power of Summary-Source Alignments",
author = "Ernst, Ori and
Shapira, Ori and
Slobodkin, Aviv and
Adar, Sharon and
Bansal, Mohit and
Goldberger, Jacob and
Levy, Ran and
Dagan, Ido",
editor = "Ku, Lun-Wei an... | Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection, followed by text generation.In this context, alignment of corresponding sentences between a reference summary and its source documents has been leveraged to generate training data for some of the... | [
"Ernst, Ori",
"Shapira, Ori",
"Slobodkin, Aviv",
"Adar, Sharon",
"Bansal, Mohit",
"Goldberger, Jacob",
"Levy, Ran",
"Dagan, Ido"
] | The Power of Summary-Source Alignments | findings-acl.389 | Poster | 2303.08494v1 |
https://aclanthology.org/2024.findings-acl.390.bib | @inproceedings{bhatt-etal-2024-experimental,
title = "An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models",
author = "Bhatt, Gantavya and
Chen, Yifang and
Das, Arnav and
Zhang, Jifan and
Truong, Sang and
Mussmann, Stephen and
... | Supervised finetuning (SFT) on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities observed in modern large language models (LLMs). However, the annotation efforts required to produce high quality responses for instructions are becoming prohibitively expensiv... | [
"Bhatt, Gantavya",
"Chen, Yifang",
"Das, Arnav",
"Zhang, Jifan",
"Truong, Sang",
"Mussmann, Stephen",
"Zhu, Yinglun",
"Bilmes, Jeff",
"Du, Simon",
"Jamieson, Kevin",
"Ash, Jordan",
"Nowak, Robert"
] | An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models | findings-acl.390 | Poster | 2407.02770v1 |
https://aclanthology.org/2024.findings-acl.391.bib | @inproceedings{tian-etal-2024-learning,
title = "Learning Multimodal Contrast with Cross-modal Memory and Reinforced Contrast Recognition",
author = "Tian, Yuanhe and
Xia, Fei and
Song, Yan",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings... | In many practical scenarios, contents from different modalities are not semantically aligned; for instance, visual and textual information may conflict with each other, resulting in non-compositional expression effects such as irony or humor. Effective modeling and smooth integration of multimodal information are cruci... | [
"Tian, Yuanhe",
"Xia, Fei",
"Song, Yan"
] | Learning Multimodal Contrast with Cross-modal Memory and Reinforced Contrast Recognition | findings-acl.391 | Poster | 2401.17032v2 |
https://aclanthology.org/2024.findings-acl.392.bib | @inproceedings{bahrainian-etal-2024-text,
title = "Text Simplification via Adaptive Teaching",
author = "Bahrainian, Seyed Ali and
Dou, Jonathan and
Eickhoff, Carsten",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for... | Text simplification is the process of rewriting a piece of text using simpler vocabulary and grammatical structure in order to make the text more accessible and understandable for a larger audience. In this paper, we introduce a new text simplification model based on the notion of adaptive teaching using a teacher netw... | [
"Bahrainian, Seyed Ali",
"Dou, Jonathan",
"Eickhoff, Carsten"
] | Text Simplification via Adaptive Teaching | findings-acl.392 | Poster | 2305.12463v1 |
https://aclanthology.org/2024.findings-acl.393.bib | @inproceedings{gokceoglu-etal-2024-multi,
title = "A multi-level multi-label text classification dataset of 19th century Ottoman and {R}ussian literary and critical texts",
author = {Gokceoglu, Gokcen and
{\c{C}}avu{\c{s}}o{\u{g}}lu, Devrim and
Akbas, Emre and
Dolcerocca, {\"O}zen},
edi... | This paper introduces a multi-level, multi-label text classification dataset comprising over 3000 documents. The dataset features literary and critical texts from 19th-century Ottoman Turkish and Russian. It is the first study to apply large language models (LLMs) to this dataset, sourced from prominent literary period... | [
"Gokceoglu, Gokcen",
"{\\c{C}}avu{\\c{s}}o{\\u{g}}lu, Devrim",
"Akbas, Emre",
"Dolcerocca, {\\\"O}zen"
] | A multi-level multi-label text classification dataset of 19th century Ottoman and {R}ussian literary and critical texts | findings-acl.393 | Poster | 2407.15136v1 |
https://aclanthology.org/2024.findings-acl.394.bib | @inproceedings{cabello-akujuobi-2024-simple,
title = "It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance",
author = "Cabello, Laura and
Akujuobi, Uchenna",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of t... | Aspect-Based Sentiment Analysis (ABSA) involves extracting opinions from textual data about specific entities and their corresponding aspects through various complementary subtasks. Several prior research has focused on developing ad hoc designs of varying complexities for these subtasks. In this paper, we build upon t... | [
"Cabello, Laura",
"Akujuobi, Uchenna"
] | It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance | findings-acl.394 | Poster | 2010.11731v2 |
https://aclanthology.org/2024.findings-acl.395.bib | @inproceedings{he-etal-2024-whose,
title = "Whose Emotions and Moral Sentiments do Language Models Reflect?",
author = "He, Zihao and
Guo, Siyi and
Rao, Ashwin and
Lerman, Kristina",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings o... | Language models (LMs) are known to represent the perspectives of some social groups better than others, which may impact their performance, especially on subjective tasks such as content moderation and hate speech detection. To explore how LMs represent different perspectives, existing research focused on positional al... | [
"He, Zihao",
"Guo, Siyi",
"Rao, Ashwin",
"Lerman, Kristina"
] | Whose Emotions and Moral Sentiments do Language Models Reflect? | findings-acl.395 | Poster | 2402.11114v2 |
https://aclanthology.org/2024.findings-acl.396.bib | @inproceedings{wang-etal-2024-llm-achieve,
title = "{LLM} can Achieve Self-Regulation via Hyperparameter Aware Generation",
author = "Wang, Siyin and
Li, Shimin and
Sun, Tianxiang and
Fu, Jinlan and
Cheng, Qinyuan and
Ye, Jiasheng and
Ye, Junjie and
Qiu, Xipeng ... | In the realm of Large Language Models (LLMs), users commonly employ diverse decoding strategies and adjust hyperparameters to control the generated text. However, a critical question emerges: Are LLMs conscious of the existence of these decoding strategies and capable of regulating themselves? The current decoding gene... | [
"Wang, Siyin",
"Li, Shimin",
"Sun, Tianxiang",
"Fu, Jinlan",
"Cheng, Qinyuan",
"Ye, Jiasheng",
"Ye, Junjie",
"Qiu, Xipeng",
"Huang, Xuanjing"
] | {LLM} can Achieve Self-Regulation via Hyperparameter Aware Generation | findings-acl.396 | Poster | 2402.11251v1 |
https://aclanthology.org/2024.findings-acl.397.bib | @inproceedings{jiang-etal-2024-forward,
title = "Forward-Backward Reasoning in Large Language Models for Mathematical Verification",
author = "Jiang, Weisen and
Shi, Han and
Yu, Longhui and
Liu, Zhengying and
Zhang, Yu and
Li, Zhenguo and
Kwok, James",
editor = "Ku,... | Self-Consistency samples diverse reasoning chains with answers and chooses the final answer by majority voting. It is based on forward reasoning and cannot further improve performance by sampling more reasoning chains when saturated. To further boost performance, we introduce backward reasoning to verify candidate answ... | [
"Jiang, Weisen",
"Shi, Han",
"Yu, Longhui",
"Liu, Zhengying",
"Zhang, Yu",
"Li, Zhenguo",
"Kwok, James"
] | Forward-Backward Reasoning in Large Language Models for Mathematical Verification | findings-acl.397 | Poster | 2405.16802v3 |
https://aclanthology.org/2024.findings-acl.398.bib | @inproceedings{han-etal-2024-towards,
title = "Towards Uncertainty-Aware Language Agent",
author = "Han, Jiuzhou and
Buntine, Wray and
Shareghi, Ehsan",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational L... | While Language Agents have achieved promising success by placing Large Language Models at the core of a more versatile design that dynamically interacts with the external world, the existing approaches neglect the notion of uncertainty during these interactions. We present the Uncertainty-Aware Language Agent (UALA), a... | [
"Han, Jiuzhou",
"Buntine, Wray",
"Shareghi, Ehsan"
] | Towards Uncertainty-Aware Language Agent | findings-acl.398 | Poster | 2404.05337v1 |
https://aclanthology.org/2024.findings-acl.399.bib | @inproceedings{lin-etal-2024-detection,
title = "Detection and Positive Reconstruction of Cognitive Distortion Sentences: {M}andarin Dataset and Evaluation",
author = "Lin, Shuya and
Wang, Yuxiong and
Dong, Jonathan and
Ni, Shiguang",
editor = "Ku, Lun-Wei and
Martins, Andre and... | This research introduces a Positive Reconstruction Framework based on positive psychology theory. Overcoming negative thoughts can be challenging, our objective is to address and reframe them through a positive reinterpretation. To tackle this challenge, a two-fold approach is necessary: identifying cognitive distortio... | [
"Lin, Shuya",
"Wang, Yuxiong",
"Dong, Jonathan",
"Ni, Shiguang"
] | Detection and Positive Reconstruction of Cognitive Distortion Sentences: {M}andarin Dataset and Evaluation | findings-acl.399 | Poster | 2405.15334v1 |
https://aclanthology.org/2024.findings-acl.400.bib | @inproceedings{han-etal-2024-pive,
title = "{P}i{V}e: Prompting with Iterative Verification Improving Graph-based Generative Capability of {LLM}s",
author = "Han, Jiuzhou and
Collier, Nigel and
Buntine, Wray and
Shareghi, Ehsan",
editor = "Ku, Lun-Wei and
Martins, Andre and
... | Large language models (LLMs) have shown great abilities of solving various natural language tasks in different domains. Due to the training objective of LLMs and their pre-training data, LLMs are not very well equipped for tasks involving structured data generation. We propose a framework, Prompting with Iterative Veri... | [
"Han, Jiuzhou",
"Collier, Nigel",
"Buntine, Wray",
"Shareghi, Ehsan"
] | {P}i{V}e: Prompting with Iterative Verification Improving Graph-based Generative Capability of {LLM}s | findings-acl.400 | Poster | 2305.12392v3 |
https://aclanthology.org/2024.findings-acl.401.bib | @inproceedings{gao-etal-2024-two,
title = "Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models",
author = "Gao, Yifu and
Qiao, Linbo and
Kan, Zhigang and
Wen, Zhihua and
He, Yongquan and
Li, Dongsheng",
editor = "Ku, Lun-Wei and... | Temporal knowledge graph question answering (TKGQA) poses a significant challenge task, due to the temporal constraints hidden in questions and the answers sought from dynamic structured knowledge. Although large language models (LLMs) have made considerable progress in their reasoning ability over structured data, the... | [
"Gao, Yifu",
"Qiao, Linbo",
"Kan, Zhigang",
"Wen, Zhihua",
"He, Yongquan",
"Li, Dongsheng"
] | Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models | findings-acl.401 | Poster | 2402.16568v2 |
https://aclanthology.org/2024.findings-acl.402.bib | @inproceedings{akter-etal-2024-visreas,
title = "{VISREAS}: Complex Visual Reasoning with Unanswerable Questions",
author = "Akter, Syeda Nahida and
Lee, Sangwu and
Chang, Yingshan and
Bisk, Yonatan and
Nyberg, Eric",
editor = "Ku, Lun-Wei and
Martins, Andre and
Sri... | Verifying a question{'}s validity before answering is crucial in real-world applications, where users may provide imperfect instructions. In this scenario, an ideal model should address the discrepancies in the query and convey them to the users rather than generating the best possible answer. Addressing this requireme... | [
"Akter, Syeda Nahida",
"Lee, Sangwu",
"Chang, Yingshan",
"Bisk, Yonatan",
"Nyberg, Eric"
] | {VISREAS}: Complex Visual Reasoning with Unanswerable Questions | findings-acl.402 | Poster | 2212.10189v2 |
https://aclanthology.org/2024.findings-acl.403.bib | @inproceedings{hu-etal-2024-unified,
title = "A Unified Generative Framework for Bilingual Euphemism Detection and Identification",
author = "Hu, Yuxue and
Li, Junsong and
Wang, Tongguan and
Su, Dongyu and
Su, Guixin and
Sha, Ying",
editor = "Ku, Lun-Wei and
Martins... | Various euphemisms are emerging in social networks, attracting widespread attention from the natural language processing community. However, existing euphemism datasets are only domain-specific or language-specific. In addition, existing approaches to the study of euphemisms are one-sided. Either only the euphemism det... | [
"Hu, Yuxue",
"Li, Junsong",
"Wang, Tongguan",
"Su, Dongyu",
"Su, Guixin",
"Sha, Ying"
] | A Unified Generative Framework for Bilingual Euphemism Detection and Identification | findings-acl.403 | Poster | 2103.16808v1 |
https://aclanthology.org/2024.findings-acl.404.bib | @inproceedings{cong-etal-2024-styledubber,
title = "{S}tyle{D}ubber: Towards Multi-Scale Style Learning for Movie Dubbing",
author = "Cong, Gaoxiang and
Qi, Yuankai and
Li, Liang and
Beheshti, Amin and
Zhang, Zhedong and
Hengel, Anton and
Yang, Ming-Hsuan and
Yan... | Given a script, the challenge in Movie Dubbing (Visual Voice Cloning, V2C) is to generate speech that aligns well with the video in both time and emotion, based on the tone of a reference audio track. Existing state-of-the-art V2C models break the phonemes in the script according to the divisions between video frames, ... | [
"Cong, Gaoxiang",
"Qi, Yuankai",
"Li, Liang",
"Beheshti, Amin",
"Zhang, Zhedong",
"Hengel, Anton",
"Yang, Ming-Hsuan",
"Yan, Chenggang",
"Huang, Qingming"
] | {S}tyle{D}ubber: Towards Multi-Scale Style Learning for Movie Dubbing | findings-acl.404 | Poster | 2402.12636v3 |
https://aclanthology.org/2024.findings-acl.405.bib | @inproceedings{yang-liu-2024-etas,
title = "{ETAS}: Zero-Shot Transformer Architecture Search via Network Trainability and Expressivity",
author = "Yang, Jiechao and
Liu, Yong",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association f... | Transformer Architecture Search (TAS) methods aim to automate searching for the optimal Transformer architecture configurations for a given task. However, they are impeded by the prohibitive cost of evaluating Transformer architectures. Recently, several Zero-Shot TAS methods have been proposed to mitigate this problem... | [
"Yang, Jiechao",
"Liu, Yong"
] | {ETAS}: Zero-Shot Transformer Architecture Search via Network Trainability and Expressivity | findings-acl.405 | Poster | 1809.02209v2 |
https://aclanthology.org/2024.findings-acl.406.bib | @inproceedings{xu-etal-2024-reasoning,
title = "Reasoning Like a Doctor: Improving Medical Dialogue Systems via Diagnostic Reasoning Process Alignment",
author = "Xu, Kaishuai and
Cheng, Yi and
Hou, Wenjun and
Tan, Qiaoyu and
Li, Wenjie",
editor = "Ku, Lun-Wei and
Martins,... | Medical dialogue systems have attracted significant attention for their potential to act as medical assistants. Enabling these medical systems to emulate clinicians{'} diagnostic reasoning process has been the long-standing research focus. Previous studies rudimentarily realized the simulation of clinicians{'} diagnost... | [
"Xu, Kaishuai",
"Cheng, Yi",
"Hou, Wenjun",
"Tan, Qiaoyu",
"Li, Wenjie"
] | Reasoning Like a Doctor: Improving Medical Dialogue Systems via Diagnostic Reasoning Process Alignment | findings-acl.406 | Poster | 2406.13934v1 |
https://aclanthology.org/2024.findings-acl.407.bib | @inproceedings{wu-etal-2024-conceptmath,
title = "{C}oncept{M}ath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models",
author = "Wu, Yanan and
Liu, Jie and
Bu, Xingyuan and
Liu, Jiaheng and
Zhou, Zhanhui and
Zhang, Yuanxing and
... | This paper introduces ConceptMath, a bilingual (English and Chinese), fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models (LLMs). Unlike traditional benchmarks that evaluate general mathematical reasoning with an average accuracy, ConceptMath systemically organizes math pr... | [
"Wu, Yanan",
"Liu, Jie",
"Bu, Xingyuan",
"Liu, Jiaheng",
"Zhou, Zhanhui",
"Zhang, Yuanxing",
"Zhang, Chenchen",
"ZhiqiBai, ZhiqiBai",
"Chen, Haibin",
"Ge, Tiezheng",
"Ouyang, Wanli",
"Su, Wenbo",
"Zheng, Bo"
] | {C}oncept{M}ath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models | findings-acl.407 | Poster | 2402.14660v2 |
https://aclanthology.org/2024.findings-acl.408.bib | @inproceedings{chen-etal-2024-reinstruct,
title = "{REI}nstruct: Building Instruction Data from Unlabeled Corpus",
author = "Chen, Shu and
Guan, Xinyan and
Lu, Yaojie and
Lin, Hongyu and
Han, Xianpei and
Sun, Le",
editor = "Ku, Lun-Wei and
Martins, Andre and
... | Manually annotating instruction data for large language models is difficult, costly, and hard to scale. Meanwhile, current automatic annotation methods typically rely on distilling synthetic data from proprietary LLMs, which not only limits the upper bound of the quality of the instruction data but also raises potentia... | [
"Chen, Shu",
"Guan, Xinyan",
"Lu, Yaojie",
"Lin, Hongyu",
"Han, Xianpei",
"Sun, Le"
] | {REI}nstruct: Building Instruction Data from Unlabeled Corpus | findings-acl.408 | Poster | 2210.09175v1 |
https://aclanthology.org/2024.findings-acl.409.bib | @inproceedings{chen-etal-2024-learning-maximize,
title = "Learning to Maximize Mutual Information for Chain-of-Thought Distillation",
author = "Chen, Xin and
Huang, Hanxian and
Gao, Yanjun and
Wang, Yi and
Zhao, Jishen and
Ding, Ke",
editor = "Ku, Lun-Wei and
Martin... | Knowledge distillation, the technique of transferring knowledge from large, complex models to smaller ones, marks a pivotal step towards efficient AI deployment. Distilling Step-by-Step (DSS), a novel method utilizing chain-of-thought (CoT) distillation, has demonstrated promise by imbuing smaller models with the super... | [
"Chen, Xin",
"Huang, Hanxian",
"Gao, Yanjun",
"Wang, Yi",
"Zhao, Jishen",
"Ding, Ke"
] | Learning to Maximize Mutual Information for Chain-of-Thought Distillation | findings-acl.409 | Poster | 2403.03348v3 |
https://aclanthology.org/2024.findings-acl.410.bib | @inproceedings{lin-etal-2024-pemt,
title = "{PEMT}: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning",
author = "Lin, Zhisheng and
Fu, Han and
Liu, Chenghao and
Li, Zhuo and
Sun, Jianling",
editor = "Ku, Lun-Wei and
Martins, An... | Parameter-efficient fine-tuning (PEFT) has emerged as an effective method for adapting pre-trained language models to various tasks efficiently. Recently, there has been a growing interest in transferring knowledge from one or multiple tasks to the downstream target task to achieve performance improvements. However, cu... | [
"Lin, Zhisheng",
"Fu, Han",
"Liu, Chenghao",
"Li, Zhuo",
"Sun, Jianling"
] | {PEMT}: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning | findings-acl.410 | Poster | 2303.16154v1 |
https://aclanthology.org/2024.findings-acl.411.bib | @inproceedings{liu-etal-2024-mathbench,
title = "{M}ath{B}ench: Evaluating the Theory and Application Proficiency of {LLM}s with a Hierarchical Mathematics Benchmark",
author = "Liu, Hongwei and
Zheng, Zilong and
Qiao, Yuxuan and
Duan, Haodong and
Fei, Zhiwei and
Zhou, Fengzhe... | Recent advancements in large language models (LLMs) have showcased significant improvements in mathematics. However, traditional math benchmarks like GSM8k offer a unidimensional perspective, which fall short in providing a holistic assessment of the LLMs{'} math capabilities. To address this gap, we introduce MathBenc... | [
"Liu, Hongwei",
"Zheng, Zilong",
"Qiao, Yuxuan",
"Duan, Haodong",
"Fei, Zhiwei",
"Zhou, Fengzhe",
"Zhang, Wenwei",
"Zhang, Songyang",
"Lin, Dahua",
"Chen, Kai"
] | {M}ath{B}ench: Evaluating the Theory and Application Proficiency of {LLM}s with a Hierarchical Mathematics Benchmark | findings-acl.411 | Poster | 2405.12209v1 |
https://aclanthology.org/2024.findings-acl.412.bib | @inproceedings{ren-etal-2024-identifying,
title = "Identifying Semantic Induction Heads to Understand In-Context Learning",
author = "Ren, Jie and
Guo, Qipeng and
Yan, Hang and
Liu, Dongrui and
Zhang, Quanshi and
Qiu, Xipeng and
Lin, Dahua",
editor = "Ku, Lun-Wei a... | Although large language models (LLMs) have demonstrated remarkable performance, the lack of transparency in their inference logic raises concerns about their trustworthiness. To gain a better understanding of LLMs, we conduct a detailed analysis of the operations of attention heads and aim to better understand the in-c... | [
"Ren, Jie",
"Guo, Qipeng",
"Yan, Hang",
"Liu, Dongrui",
"Zhang, Quanshi",
"Qiu, Xipeng",
"Lin, Dahua"
] | Identifying Semantic Induction Heads to Understand In-Context Learning | findings-acl.412 | Poster | 2402.13055v2 |
https://aclanthology.org/2024.findings-acl.413.bib | @inproceedings{jiang-etal-2024-chinese,
title = "{C}hinese Spelling Corrector Is Just a Language Learner",
author = "Jiang, Lai and
Wu, Hongqiu and
Zhao, Hai and
Zhang, Min",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the As... | This paper emphasizes Chinese spelling correction by means of self-supervised learning, which means there are no annotated errors within the training data. Our intuition is that humans are naturally good correctors with exposure to error-free sentences, which contrasts with current unsupervised methods that strongly re... | [
"Jiang, Lai",
"Wu, Hongqiu",
"Zhao, Hai",
"Zhang, Min"
] | {C}hinese Spelling Corrector Is Just a Language Learner | findings-acl.413 | Poster | 2004.14166v2 |
https://aclanthology.org/2024.findings-acl.414.bib | @inproceedings{wu-etal-2024-logical,
title = "Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models",
author = "Wu, Junfei and
Liu, Qiang and
Wang, Ding and
Zhang, Jinghao and
Wu, Shu and
Wang, Liang and
Tan, Tieniu",
editor = "Ku, Lu... | Object hallucination has been an Achilles{'} heel which hinders the broader applications of large vision-language models (LVLMs). Object hallucination refers to the phenomenon that the LVLMs claim non-existent objects in the image. To mitigate the object hallucinations, instruction tuning and external model-based detec... | [
"Wu, Junfei",
"Liu, Qiang",
"Wang, Ding",
"Zhang, Jinghao",
"Wu, Shu",
"Wang, Liang",
"Tan, Tieniu"
] | Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models | findings-acl.414 | Poster | 2402.11622v2 |
https://aclanthology.org/2024.findings-acl.415.bib | @inproceedings{zhang-etal-2024-retrievalqa,
title = "{R}etrieval{QA}: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering",
author = "Zhang, Zihan and
Fang, Meng and
Chen, Ling",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vi... | Adaptive retrieval-augmented generation (ARAG) aims to dynamically determine the necessity of retrieval for queries instead of retrieving indiscriminately to enhance the efficiency and relevance of the sourced information. However, previous works largely overlook the evaluation of ARAG approaches, leading to their effe... | [
"Zhang, Zihan",
"Fang, Meng",
"Chen, Ling"
] | {R}etrieval{QA}: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering | findings-acl.415 | Poster | 2209.11396v1 |
https://aclanthology.org/2024.findings-acl.416.bib | @inproceedings{chen-etal-2024-llast,
title = "{LL}a{ST}: Improved End-to-end Speech Translation System Leveraged by Large Language Models",
author = "Chen, Xi and
Zhang, Songyang and
Bai, Qibing and
Chen, Kai and
Nakamura, Satoshi",
editor = "Ku, Lun-Wei and
Martins, Andre... | We introduces ***LLaST***, a framework for building high-performance Large Language model based Speech-to-text Translation systems. We address the limitations of end-to-end speech translation (E2E ST) models by exploring model architecture design and optimization techniques tailored for LLMs. Our approach includes LLM-... | [
"Chen, Xi",
"Zhang, Songyang",
"Bai, Qibing",
"Chen, Kai",
"Nakamura, Satoshi"
] | {LL}a{ST}: Improved End-to-end Speech Translation System Leveraged by Large Language Models | findings-acl.416 | Poster | 2107.06959v2 |
https://aclanthology.org/2024.findings-acl.417.bib | @inproceedings{gu-yang-2024-plan,
title = "Plan, Generate and Complicate: Improving Low-resource Dialogue State Tracking via Easy-to-Difficult Zero-shot Data Augmentation",
author = "Gu, Ming and
Yang, Yan",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = ... | Data augmentation methods have been a promising direction to improve the performance of small models for low-resource dialogue state tracking. However, traditional methods rely on pre-defined user goals and neglect the importance of data complexity in this task. In this paper, we propose EDZ-DA, an Easy-to-Difficult Ze... | [
"Gu, Ming",
"Yang, Yan"
] | Plan, Generate and Complicate: Improving Low-resource Dialogue State Tracking via Easy-to-Difficult Zero-shot Data Augmentation | findings-acl.417 | Poster | 2406.08860v1 |
https://aclanthology.org/2024.findings-acl.418.bib | @inproceedings{quan-2024-dmoerm,
title = "{DM}o{ERM}: Recipes of Mixture-of-Experts for Effective Reward Modeling",
author = "Quan, Shanghaoran",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",... | The performance of the reward model (RM) is a critical factor in improving the effectiveness of the large language model (LLM) during alignment fine-tuning. There remain two challenges in RM training: 1) training the same RM using various categories of data may cause its generalization performance to suffer from multi-... | [
"Quan, Shanghaoran"
] | {DM}o{ERM}: Recipes of Mixture-of-Experts for Effective Reward Modeling | findings-acl.418 | Poster | 2407.04185v2 |
https://aclanthology.org/2024.findings-acl.419.bib | @inproceedings{yamada-ri-2024-leia,
title = "{LEIA}: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation",
author = "Yamada, Ikuya and
Ri, Ryokan",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings o... | Adapting English-based large language models (LLMs) to other languages has become increasingly popular due to the efficiency and potential of cross-lingual transfer. However, existing language adaptation methods often overlook the benefits of cross-lingual supervision. In this study, we introduce LEIA, a language adapt... | [
"Yamada, Ikuya",
"Ri, Ryokan"
] | {LEIA}: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation | findings-acl.419 | Poster | 2309.12763v2 |
https://aclanthology.org/2024.findings-acl.420.bib | @inproceedings{chen-etal-2024-comments,
title = "Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective",
author = "Chen, Yijie and
Liu, Yijin and
Meng, Fandong and
Chen, Yufeng and
Xu, Jinan and
Zhou, Jie",
editor = "Ku, Lun-Wei and
Marti... | Code generation aims to understand the problem description and generate corresponding code snippets, where existing works generally decompose such complex tasks into intermediate steps by prompting strategies, such as Chain-of-Thought and its variants. While these studies have achieved some success, their effectiveness... | [
"Chen, Yijie",
"Liu, Yijin",
"Meng, F",
"ong",
"Chen, Yufeng",
"Xu, Jinan",
"Zhou, Jie"
] | Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective | findings-acl.420 | Poster | 2404.07549v1 |
https://aclanthology.org/2024.findings-acl.421.bib | @inproceedings{dai-etal-2024-cocktail,
title = "Cocktail: A Comprehensive Information Retrieval Benchmark with {LLM}-Generated Documents Integration",
author = "Dai, Sunhao and
Liu, Weihao and
Zhou, Yuqi and
Pang, Liang and
Ruan, Rongju and
Wang, Gang and
Dong, Zhenhua ... | The proliferation of Large Language Models (LLMs) has led to an influx of AI-generated content (AIGC) on the internet, transforming the corpus of Information Retrieval (IR) systems from solely human-written to a coexistence with LLM-generated content. The impact of this surge in AIGC on IR systems remains an open quest... | [
"Dai, Sunhao",
"Liu, Weihao",
"Zhou, Yuqi",
"Pang, Liang",
"Ruan, Rongju",
"Wang, Gang",
"Dong, Zhenhua",
"Xu, Jun",
"Wen, Ji-Rong"
] | Cocktail: A Comprehensive Information Retrieval Benchmark with {LLM}-Generated Documents Integration | findings-acl.421 | Poster | 2405.16546v2 |
https://aclanthology.org/2024.findings-acl.422.bib | @inproceedings{feng-etal-2024-continual,
title = "Continual Dialogue State Tracking via Reason-of-Select Distillation",
author = "Feng, Yujie and
Liu, Bo and
Dong, Xiaoyu and
Lu, Zexin and
Zhan, Li-Ming and
Wu, Xiao-Ming and
Lam, Albert",
editor = "Ku, Lun-Wei and
... | An ideal dialogue system requires continuous skill acquisition and adaptation to new tasks while retaining prior knowledge. Dialogue State Tracking (DST), vital in these systems, often involves learning new services, confronting catastrophic forgetting and a critical capability loss termed the {``}Value Selection Quand... | [
"Feng, Yujie",
"Liu, Bo",
"Dong, Xiaoyu",
"Lu, Zexin",
"Zhan, Li-Ming",
"Wu, Xiao-Ming",
"Lam, Albert"
] | Continual Dialogue State Tracking via Reason-of-Select Distillation | findings-acl.422 | Poster | 2302.08220v2 |
https://aclanthology.org/2024.findings-acl.423.bib | @inproceedings{li-etal-2024-spotting,
title = "Spotting {AI}{'}s Touch: Identifying {LLM}-Paraphrased Spans in Text",
author = "Li, Yafu and
Wang, Zhilin and
Cui, Leyang and
Bi, Wei and
Shi, Shuming and
Zhang, Yue",
editor = "Ku, Lun-Wei and
Martins, Andre and
... | AI-generated text detection has attracted increasing attention as powerful language models approach human-level generation. Limited work is devoted to detecting (partially) AI-paraphrased texts. However, AI paraphrasing is commonly employed in various application scenarios for text refinement and diversity. To this end... | [
"Li, Yafu",
"Wang, Zhilin",
"Cui, Leyang",
"Bi, Wei",
"Shi, Shuming",
"Zhang, Yue"
] | Spotting {AI}{'}s Touch: Identifying {LLM}-Paraphrased Spans in Text | findings-acl.423 | Poster | 2405.12689v2 |
https://aclanthology.org/2024.findings-acl.424.bib | @inproceedings{lu-etal-2024-sofa,
title = "{S}o{FA}: Shielded On-the-fly Alignment via Priority Rule Following",
author = "Lu, Xinyu and
Yu, Bowen and
Lu, Yaojie and
Lin, Hongyu and
Yu, Haiyang and
Sun, Le and
Han, Xianpei and
Li, Yongbin",
editor = "Ku, Lun-... | The alignment problem in Large Language Models (LLMs) involves adapting them to the broad spectrum of human values. This requirement challenges existing alignment methods due to diversity of preferences and regulatory standards. This paper introduces a novel alignment paradigm, priority rule following, which defines ru... | [
"Lu, Xinyu",
"Yu, Bowen",
"Lu, Yaojie",
"Lin, Hongyu",
"Yu, Haiyang",
"Sun, Le",
"Han, Xianpei",
"Li, Yongbin"
] | {S}o{FA}: Shielded On-the-fly Alignment via Priority Rule Following | findings-acl.424 | Poster | 2402.17358v1 |
https://aclanthology.org/2024.findings-acl.425.bib | @inproceedings{goldstein-stanovsky-2024-zombies,
title = "Do Zombies Understand? A Choose-Your-Own-Adventure Exploration of Machine Cognition",
author = "Goldstein, Ariel and
Stanovsky, Gabriel",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of... | Recent advances in LLMs have sparked a debate on whether they understand text. In this position paper, we argue that opponents in this debate hold different definitions for understanding, and particularly differ in their view on the role of consciousness. To substantiate this claim, we propose a thought experiment invo... | [
"Goldstein, Ariel",
"Stanovsky, Gabriel"
] | Do Zombies Understand? A Choose-Your-Own-Adventure Exploration of Machine Cognition | findings-acl.425 | Poster | 2403.00499v2 |
https://aclanthology.org/2024.findings-acl.426.bib | @inproceedings{christ-etal-2024-modeling,
title = "Modeling Emotional Trajectories in Written Stories Utilizing Transformers and Weakly-Supervised Learning",
author = {Christ, Lukas and
Amiriparian, Shahin and
Milling, Manuel and
Aslan, Ilhan and
Schuller, Bj{\"o}rn},
editor = "K... | Telling stories is an integral part of human communication which can evoke emotions and influence the affective states of the audience. Automatically modeling emotional trajectories in stories has thus attracted considerable scholarly interest. However, as most existing works have been limited to unsupervised dictionar... | [
"Christ, Lukas",
"Amiriparian, Shahin",
"Milling, Manuel",
"Aslan, Ilhan",
"Schuller, Bj{\\\"o}rn"
] | Modeling Emotional Trajectories in Written Stories Utilizing Transformers and Weakly-Supervised Learning | findings-acl.426 | Poster | 2406.02251v1 |
https://aclanthology.org/2024.findings-acl.427.bib | @inproceedings{cao-etal-2024-rap,
title = "{RAP}: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter",
author = "Cao, Meng and
Tang, Haoran and
Huang, Jinfa and
Jin, Peng and
Zhang, Can and
Liu, Ruyang and
Chen, Long and
Liang, Xiaodan and
Y... | Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries. To date, most of the state-of-the-art TVR methods learn image-to-video transfer learning based on the large-scale pre-trained vision-language models (e.g., CLIP). However, fully fine-tuning these pre-trained models for TVR in... | [
"Cao, Meng",
"Tang, Haoran",
"Huang, Jinfa",
"Jin, Peng",
"Zhang, Can",
"Liu, Ruyang",
"Chen, Long",
"Liang, Xiaodan",
"Yuan, Li",
"Li, Ge"
] | {RAP}: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter | findings-acl.427 | Poster | 2303.13220v1 |
https://aclanthology.org/2024.findings-acl.428.bib | @inproceedings{wang-etal-2024-benchmarking,
title = "Benchmarking and Improving Long-Text Translation with Large Language Models",
author = "Wang, Longyue and
Du, Zefeng and
Jiao, Wenxiang and
Lyu, Chenyang and
Pang, Jianhui and
Cui, Leyang and
Song, Kaiqiang and
... | Recent studies have illuminated the promising capabilities of large language models (LLMs) in handling long texts. However, their performance in machine translation (MT) of long documents remains underexplored. This paper aims to shed light on how LLMs navigate this complex task, offering a comprehensive evaluation of ... | [
"Wang, Longyue",
"Du, Zefeng",
"Jiao, Wenxiang",
"Lyu, Chenyang",
"Pang, Jianhui",
"Cui, Leyang",
"Song, Kaiqiang",
"Wong, Derek",
"Shi, Shuming",
"Tu, Zhaopeng"
] | Benchmarking and Improving Long-Text Translation with Large Language Models | findings-acl.428 | Poster | 2405.04164v1 |
https://aclanthology.org/2024.findings-acl.429.bib | @inproceedings{fan-etal-2024-personalized,
title = "Personalized Topic Selection Model for Topic-Grounded Dialogue",
author = "Fan, Shixuan and
Wei, Wei and
Wen, Xiaofei and
Mao, Xian-Ling and
Chen, Jixiong and
Chen, Dangyang",
editor = "Ku, Lun-Wei and
Martins, And... | Recently, the topic-grounded dialogue (TGD) system has become increasingly popular as its powerful capability to actively guide users to accomplish specific tasks through topic-guided conversations. Most existing works utilize side information (e.g. topics or personas) in isolation to enhance the topic selection abilit... | [
"Fan, Shixuan",
"Wei, Wei",
"Wen, Xiaofei",
"Mao, Xian-Ling",
"Chen, Jixiong",
"Chen, Dangyang"
] | Personalized Topic Selection Model for Topic-Grounded Dialogue | findings-acl.429 | Poster | 2406.01988v1 |
https://aclanthology.org/2024.findings-acl.430.bib | @inproceedings{li-etal-2024-debiasing,
title = "Debiasing In-Context Learning by Instructing {LLM}s How to Follow Demonstrations",
author = "Li, Lvxue and
Chen, Jiaqi and
Lu, Xinyu and
Lu, Yaojie and
Lin, Hongyu and
Zhou, Shuheng and
Zhu, Huijia and
Wang, Weiqian... | In-context learning(ICL) has gained considerable attention due to its data efficiency and task adaptability. Unfortunately, ICL suffers from the demonstration bias, i.e., its performance and robustness are severely affected by the selection and ordering of demonstrations. In this paper, we identify that such demonstrat... | [
"Li, Lvxue",
"Chen, Jiaqi",
"Lu, Xinyu",
"Lu, Yaojie",
"Lin, Hongyu",
"Zhou, Shuheng",
"Zhu, Huijia",
"Wang, Weiqiang",
"Liu, Zhongyi",
"Han, Xianpei",
"Sun, Le"
] | Debiasing In-Context Learning by Instructing {LLM}s How to Follow Demonstrations | findings-acl.430 | Poster | 2407.02030v1 |
https://aclanthology.org/2024.findings-acl.431.bib | @inproceedings{vlachos-etal-2024-comparing,
title = "Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog Systems",
author = "Vlachos, Christos and
Stafylakis, Themos and
Androutsopoulos, Ion",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
... | Creating effective and reliable task-oriented dialog systems (ToDSs) is challenging, not only because of the complex structure of these systems, but also due to the scarcity of training data, especially when several modules need to be trained separately, each one with its own input/output training examples. Data augmen... | [
"Vlachos, Christos",
"Stafylakis, Themos",
"Androutsopoulos, Ion"
] | Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog Systems | findings-acl.431 | Poster | 2310.10380v1 |
https://aclanthology.org/2024.findings-acl.432.bib | @inproceedings{ma-etal-2024-ms2sl,
title = "{MS}2{SL}: Multimodal Spoken Data-Driven Continuous Sign Language Production",
author = "Ma, Jian and
Wang, Wenguan and
Yang, Yi and
Zheng, Feng",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Fi... | Sign language understanding has made significant strides; however, there is still no viable solution for generating sign sequences directlyfrom entire spoken content, e.g., text or speech. In this paper, we propose a unified framework for continuous sign language production, easing communication between sign and non-si... | [
"Ma, Jian",
"Wang, Wenguan",
"Yang, Yi",
"Zheng, Feng"
] | {MS}2{SL}: Multimodal Spoken Data-Driven Continuous Sign Language Production | findings-acl.432 | Poster | 2407.12842v1 |
https://aclanthology.org/2024.findings-acl.433.bib | @inproceedings{zhao-etal-2024-bba,
title = "{BBA}: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models",
author = "Zhao, Xueliang and
Huang, Xinting and
Fu, Tingchen and
Li, Qintong and
Gong, Shansan and
Liu, Lemao and
Bi, Wei and
Kong, ... | Multimodal reasoning stands as a pivotal capability for large vision-language models (LVLMs). The integration with Domain-Specific Languages (DSL), offering precise visual representations, equips these models with the opportunity to execute more accurate reasoning in complex and professional domains. However, the vanil... | [
"Zhao, Xueliang",
"Huang, Xinting",
"Fu, Tingchen",
"Li, Qintong",
"Gong, Shansan",
"Liu, Lemao",
"Bi, Wei",
"Kong, Lingpeng"
] | {BBA}: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models | findings-acl.433 | Poster | 2311.10947v2 |
https://aclanthology.org/2024.findings-acl.434.bib | @inproceedings{zheng-etal-2024-partialformer,
title = "{P}artial{F}ormer: Modeling Part Instead of Whole for Machine Translation",
author = "Zheng, Tong and
Li, Bei and
Bao, Huiwen and
Wang, Jiale and
Shan, Weiqiao and
Xiao, Tong and
Zhu, JingBo",
editor = "Ku, Lun-... | The design choices in Transformer feed-forward neural networks have resulted in significant computational and parameter overhead. In this work, we emphasize the importance of hidden dimensions in designing lightweight FFNs, a factor often overlooked in previous architectures. Guided by this principle, we introduce Part... | [
"Zheng, Tong",
"Li, Bei",
"Bao, Huiwen",
"Wang, Jiale",
"Shan, Weiqiao",
"Xiao, Tong",
"Zhu, JingBo"
] | {P}artial{F}ormer: Modeling Part Instead of Whole for Machine Translation | findings-acl.434 | Poster | 2310.14921v2 |
https://aclanthology.org/2024.findings-acl.435.bib | @inproceedings{kim-etal-2024-self-consistent,
title = "Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy",
author = "Kim, Jieyong and
Heo, Ryang and
Seo, Yongsik and
Kang, SeongKu and
Yeo, Jinyoung and
Lee, Dongha",
editor =... | In the task of aspect sentiment quad prediction (ASQP), generative methods for predicting sentiment quads have shown promisingresults. However, they still suffer from imprecise predictions and limited interpretability, caused by data scarcity and inadequate modeling of the quadruplet composition process. In this paper,... | [
"Kim, Jieyong",
"Heo, Ryang",
"Seo, Yongsik",
"Kang, SeongKu",
"Yeo, Jinyoung",
"Lee, Dongha"
] | Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy | findings-acl.435 | Poster | 2403.00354v2 |
https://aclanthology.org/2024.findings-acl.436.bib | @inproceedings{dong-etal-2024-pace,
title = "{PACE}: Improving Prompt with Actor-Critic Editing for Large Language Model",
author = "Dong, Yihong and
Luo, Kangcheng and
Jiang, Xue and
Jin, Zhi and
Li, Ge",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek... | Large language models (LLMs) have showcased remarkable potential across various tasks by conditioning on prompts. However, the quality of different human-written prompts leads to substantial discrepancies in LLMs{'} performance, and improving prompts usually necessitates considerable human effort and expertise. To this... | [
"Dong, Yihong",
"Luo, Kangcheng",
"Jiang, Xue",
"Jin, Zhi",
"Li, Ge"
] | {PACE}: Improving Prompt with Actor-Critic Editing for Large Language Model | findings-acl.436 | Poster | 2308.10088v2 |
https://aclanthology.org/2024.findings-acl.437.bib | @inproceedings{xu-etal-2024-penetrative,
title = "Penetrative {AI}: Making {LLM}s Comprehend the Physical World",
author = "Xu, Huatao and
Han, Liying and
Yang, Qirui and
Li, Mo and
Srivastava, Mani",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
... | Recent developments in Large Language Models (LLMs) have demonstrated their remarkable capabilities across a range of tasks. Questions, however, persist about the nature of LLMs and their potential to integrate common-sense human knowledge when performing tasks involving information about the real physical world. This ... | [
"Xu, Huatao",
"Han, Liying",
"Yang, Qirui",
"Li, Mo",
"Srivastava, Mani"
] | Penetrative {AI}: Making {LLM}s Comprehend the Physical World | findings-acl.437 | Poster | 2310.09605v3 |
https://aclanthology.org/2024.findings-acl.438.bib | @inproceedings{zhang-etal-2024-impact,
title = "The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional Analysis",
author = "Zhang, Miaoran and
Gautam, Vagrant and
Wang, Mingyang and
Alabi, Jesujoba and
Shen, Xiaoyu and
Klakow, Dietrich and
... | In-context learning is a popular inference strategy where large language models solve a task using only a few labeled demonstrations without needing any parameter updates. Although there have been extensive studies on English in-context learning, multilingual in-context learning remains under-explored, and we lack an i... | [
"Zhang, Miaoran",
"Gautam, Vagrant",
"Wang, Mingyang",
"Alabi, Jesujoba",
"Shen, Xiaoyu",
"Klakow, Dietrich",
"Mosbach, Marius"
] | The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional Analysis | findings-acl.438 | Poster | 2402.12976v2 |
https://aclanthology.org/2024.findings-acl.439.bib | @inproceedings{dong-etal-2024-rich,
title = "Rich Semantic Knowledge Enhanced Large Language Models for Few-shot {C}hinese Spell Checking",
author = "Dong, Ming and
Chen, Yujing and
Zhang, Miao and
Sun, Hao and
He, Tingting",
editor = "Ku, Lun-Wei and
Martins, Andre and
... | Chinese Spell Checking (CSC) is a widely used technology, which plays a vital role in speech to text (STT) and optical character recognition (OCR). Most of the existing CSC approaches relying on BERT architecture achieve excellent performance. However, limited by the scale of the foundation model, BERT-based method doe... | [
"Dong, Ming",
"Chen, Yujing",
"Zhang, Miao",
"Sun, Hao",
"He, Tingting"
] | Rich Semantic Knowledge Enhanced Large Language Models for Few-shot {C}hinese Spell Checking | findings-acl.439 | Poster | 2403.08492v2 |
https://aclanthology.org/2024.findings-acl.440.bib | @inproceedings{chitale-etal-2024-empirical,
title = "An Empirical Study of In-context Learning in {LLM}s for Machine Translation",
author = "Chitale, Pranjal and
Gala, Jay and
Dabre, Raj",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of... | Recent interest has surged in employing Large Language Models (LLMs) for machine translation (MT) via in-context learning (ICL) (Vilar et al., 2023). Most prior studies primarily focus on optimizing translation quality, with limited attention to understanding the specific aspects of ICL that influence the said quality.... | [
"Chitale, Pranjal",
"Gala, Jay",
"Dabre, Raj"
] | An Empirical Study of In-context Learning in {LLM}s for Machine Translation | findings-acl.440 | Poster | 2402.10207v5 |
https://aclanthology.org/2024.findings-acl.441.bib | @inproceedings{wang-etal-2024-answer-c,
title = "{``}My Answer is {C}{''}: First-Token Probabilities Do Not Match Text Answers in Instruction-Tuned Language Models",
author = {Wang, Xinpeng and
Ma, Bolei and
Hu, Chengzhi and
Weber-Genzel, Leon and
R{\"o}ttger, Paul and
Kreuter... | The open-ended nature of language generation makes the evaluation of autoregressive large language models (LLMs) challenging. One common evaluation approach uses multiple-choice questions to limit the response space. The model is then evaluated by ranking the candidate answers by the log probability of the first token ... | [
"Wang, Xinpeng",
"Ma, Bolei",
"Hu, Chengzhi",
"Weber-Genzel, Leon",
"R{\\\"o}ttger, Paul",
"Kreuter, Frauke",
"Hovy, Dirk",
"Plank, Barbara"
] | {``}My Answer is {C}{''}: First-Token Probabilities Do Not Match Text Answers in Instruction-Tuned Language Models | findings-acl.441 | Poster | 2404.08382v1 |
https://aclanthology.org/2024.findings-acl.442.bib | @inproceedings{sun-etal-2024-oda,
title = "{ODA}: Observation-Driven Agent for integrating {LLM}s and Knowledge Graphs",
author = "Sun, Lei and
Tao, Zhengwei and
Li, Youdi and
Arakawa, Hiroshi",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle =... | The integration of Large Language Models (LLMs) and knowledge graphs (KGs) has achieved remarkable success in various natural language processing tasks. However, existing methodologies that integrate LLMs and KGs often navigate the task-solving process solely based on the LLM{'}s analysis of the question, overlooking t... | [
"Sun, Lei",
"Tao, Zhengwei",
"Li, Youdi",
"Arakawa, Hiroshi"
] | {ODA}: Observation-Driven Agent for integrating {LLM}s and Knowledge Graphs | findings-acl.442 | Poster | 2402.11163v1 |
https://aclanthology.org/2024.findings-acl.443.bib | @inproceedings{xu-etal-2024-comprehensive,
title = "A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models",
author = "Xu, Zihao and
Liu, Yi and
Deng, Gelei and
Li, Yuekang and
Picek, Stjepan",
editor = "Ku, Lun-Wei and
Martins, Andre and
... | Large Language Models (LLMs) have increasingly become central to generating content with potential societal impacts. Notably, these models have demonstrated capabilities for generating content that could be deemed harmful. To mitigate these risks, researchers have adopted safety training techniques to align model outpu... | [
"Xu, Zihao",
"Liu, Yi",
"Deng, Gelei",
"Li, Yuekang",
"Picek, Stjepan"
] | A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models | findings-acl.443 | Poster | 2401.16765v1 |
https://aclanthology.org/2024.findings-acl.444.bib | @inproceedings{kaliosis-etal-2024-data,
title = "A Data-Driven Guided Decoding Mechanism for Diagnostic Captioning",
author = "Kaliosis, Panagiotis and
Pavlopoulos, John and
Charalampakos, Foivos and
Moschovis, Georgios and
Androutsopoulos, Ion",
editor = "Ku, Lun-Wei and
... | Diagnostic Captioning (DC) automatically generates a diagnostic text from one or more medical images (e.g., X-rays, MRIs) of a patient. Treated as a draft, the generated text may assist clinicians, by providing an initial estimation of the patient{'}s condition, speeding up and helping safeguard the diagnostic process.... | [
"Kaliosis, Panagiotis",
"Pavlopoulos, John",
"Charalampakos, Foivos",
"Moschovis, Georgios",
"Androutsopoulos, Ion"
] | A Data-Driven Guided Decoding Mechanism for Diagnostic Captioning | findings-acl.444 | Poster | 2406.14164v1 |
https://aclanthology.org/2024.findings-acl.445.bib | @inproceedings{zhang-etal-2024-balancing,
title = "Balancing Speciality and Versatility: a Coarse to Fine Framework for Supervised Fine-tuning Large Language Model",
author = "Zhang, Hengyuan and
Wu, Yanru and
Li, Dawei and
Yang, Sak and
Zhao, Rui and
Jiang, Yong and
Ta... | Aligned Large Language Models (LLMs) showcase remarkable versatility, capable of handling diverse real-world tasks. Meanwhile, aligned LLMs are also expected to exhibit speciality, excelling in specific applications. However, fine-tuning with extra data, a common practice to gain speciality, often leads to catastrophic... | [
"Zhang, Hengyuan",
"Wu, Yanru",
"Li, Dawei",
"Yang, Sak",
"Zhao, Rui",
"Jiang, Yong",
"Tan, Fei"
] | Balancing Speciality and Versatility: a Coarse to Fine Framework for Supervised Fine-tuning Large Language Model | findings-acl.445 | Poster | 2404.10306v5 |
https://aclanthology.org/2024.findings-acl.446.bib | @inproceedings{xu-etal-2024-two,
title = "A Two-Agent Game for Zero-shot Relation Triplet Extraction",
author = "Xu, Ting and
Yang, Haiqin and
Zhao, Fei and
Wu, Zhen and
Dai, Xinyu",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Fin... | Relation triplet extraction is a fundamental task in natural language processing that aims to identify semantic relationships between entities in text. It is particularly challenging in the zero-shot setting, i.e., zero-shot relation triplet extraction (ZeroRTE), where the relation sets between training and test are di... | [
"Xu, Ting",
"Yang, Haiqin",
"Zhao, Fei",
"Wu, Zhen",
"Dai, Xinyu"
] | A Two-Agent Game for Zero-shot Relation Triplet Extraction | findings-acl.446 | Poster | 2010.02609v3 |
https://aclanthology.org/2024.findings-acl.447.bib | @inproceedings{gu-etal-2024-light,
title = "Light-{PEFT}: Lightening Parameter-Efficient Fine-Tuning via Early Pruning",
author = "Gu, Naibin and
Fu, Peng and
Liu, Xiyu and
Shen, Bowen and
Lin, Zheng and
Wang, Weiping",
editor = "Ku, Lun-Wei and
Martins, Andre and
... | Parameter-efficient fine-tuning (PEFT) has emerged as the predominant technique for fine-tuning in the era of large language models. However, existing PEFT methods still have inadequate training efficiency. Firstly, the utilization of large-scale foundation models during the training process is excessively redundant fo... | [
"Gu, Naibin",
"Fu, Peng",
"Liu, Xiyu",
"Shen, Bowen",
"Lin, Zheng",
"Wang, Weiping"
] | Light-{PEFT}: Lightening Parameter-Efficient Fine-Tuning via Early Pruning | findings-acl.447 | Poster | 2110.12007v1 |
https://aclanthology.org/2024.findings-acl.448.bib | @inproceedings{lardelli-etal-2024-building,
title = "Building Bridges: A Dataset for Evaluating Gender-Fair Machine Translation into {G}erman",
author = "Lardelli, Manuel and
Attanasio, Giuseppe and
Lauscher, Anne",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
... | The translation of gender-neutral person-referring terms (e.g.,the students) is often non-trivial.Translating from English into German poses an interesting case{---}in German, person-referring nouns are usually gender-specific, and if the gender of the referent(s) is unknown or diverse, the generic masculine (die Stude... | [
"Lardelli, Manuel",
"Attanasio, Giuseppe",
"Lauscher, Anne"
] | Building Bridges: A Dataset for Evaluating Gender-Fair Machine Translation into {G}erman | findings-acl.448 | Poster | 2406.06131v1 |
https://aclanthology.org/2024.findings-acl.449.bib | @inproceedings{sun-etal-2024-prompt,
title = "Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization",
author = "Sun, Shichao and
Yuan, Ruifeng and
Cao, Ziqiang and
Li, Wenjie and
Liu, Pengfei",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vi... | Large language models (LLMs) have demonstrated the capacity to improve summary quality by mirroring a human-like iterative process of critique and refinement starting from the initial draft. Two strategies are designed to perform this iterative process: $\textit{Prompt Chaining}$ and $\textit{Stepwise Prompt}$. Prompt ... | [
"Sun, Shichao",
"Yuan, Ruifeng",
"Cao, Ziqiang",
"Li, Wenjie",
"Liu, Pengfei"
] | Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization | findings-acl.449 | Poster | 2406.00507v1 |
https://aclanthology.org/2024.findings-acl.450.bib | @inproceedings{long-etal-2024-trust,
title = "Trust in Internal or External Knowledge? Generative Multi-Modal Entity Linking with Knowledge Retriever",
author = "Long, Xinwei and
Zeng, Jiali and
Meng, Fandong and
Zhou, Jie and
Zhou, Bowen",
editor = "Ku, Lun-Wei and
Martin... | Multi-modal entity linking (MEL) is a challenging task that requires accurate prediction of entities within extensive search spaces, utilizing multi-modal contexts. Existing generative approaches struggle with the knowledge gap between visual entity information and the intrinsic parametric knowledge of LLMs. To address... | [
"Long, Xinwei",
"Zeng, Jiali",
"Meng, F",
"ong",
"Zhou, Jie",
"Zhou, Bowen"
] | Trust in Internal or External Knowledge? Generative Multi-Modal Entity Linking with Knowledge Retriever | findings-acl.450 | Poster | 1810.10004v1 |
https://aclanthology.org/2024.findings-acl.451.bib | @inproceedings{aida-bollegala-2024-semantic,
title = "A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection",
author = "Aida, Taichi and
Bollegala, Danushka",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Ass... | Detecting temporal semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions.Lexical Semantic Change Detection (SCD) task involves predicting whether a given target word, $w$, changes its meaning between two different text corpora, $C_1$ and $C_2$.For this pur... | [
"Aida, Taichi",
"Bollegala, Danushka"
] | A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection | findings-acl.451 | Poster | 2403.00226v3 |
https://aclanthology.org/2024.findings-acl.452.bib | @inproceedings{li-etal-2024-achieved,
title = "What Have We Achieved on Non-autoregressive Translation?",
author = "Li, Yafu and
Zhang, Huajian and
Yan, Jianhao and
Yin, Yongjing and
Zhang, Yue",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
boo... | Recent advances have made non-autoregressive (NAT) translation comparable to autoregressive methods (AT). However, their evaluation using BLEU has been shown to weakly correlate with human annotations. Limited research compares non-autoregressive translation and autoregressive translation comprehensively, leaving uncer... | [
"Li, Yafu",
"Zhang, Huajian",
"Yan, Jianhao",
"Yin, Yongjing",
"Zhang, Yue"
] | What Have We Achieved on Non-autoregressive Translation? | findings-acl.452 | Poster | 2406.14267v1 |
https://aclanthology.org/2024.findings-acl.453.bib | @inproceedings{reiss-etal-2024-zero,
title = "From Zero to Hero: Cold-Start Anomaly Detection",
author = "Reiss, Tal and
Kour, George and
Zwerdling, Naama and
Anaby Tavor, Ateret and
Hoshen, Yedid",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
... | When first deploying an anomaly detection system, e.g., to detect out-of-scope queries in chatbots, there are no observed data, making data-driven approaches ineffective. Zero-shot anomaly detection methods offer a solution to such {``}cold-start{''} cases, but unfortunately they are often not accurate enough. This pap... | [
"Reiss, Tal",
"Kour, George",
"Zwerdling, Naama",
"Anaby Tavor, Ateret",
"Hoshen, Yedid"
] | From Zero to Hero: Cold-Start Anomaly Detection | findings-acl.453 | Poster | 2306.09067v2 |
https://aclanthology.org/2024.findings-acl.454.bib | @inproceedings{zhao-etal-2024-large,
title = "Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives",
author = "Zhao, Runcong and
Zhu, Qinglin and
Xu, Hainiu and
Li, Jiazheng and
Zhou, Yuxiang and
He, Yulan and
Gui, Lin",
edit... | Existing datasets for narrative understanding often fail to represent the complexity and uncertainty of relationships in real-life social scenarios. To address this gap, we introduce a new benchmark, Conan, designed for extracting and analysing intricate character relation graphs from detective narratives. Specifically... | [
"Zhao, Runcong",
"Zhu, Qinglin",
"Xu, Hainiu",
"Li, Jiazheng",
"Zhou, Yuxiang",
"He, Yulan",
"Gui, Lin"
] | Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives | findings-acl.454 | Poster | 2402.11051v1 |
https://aclanthology.org/2024.findings-acl.455.bib | @inproceedings{qiao-etal-2024-distillmike,
title = "{D}istill{MIKE}: Editing Distillation of Massive In-Context Knowledge Editing in Large Language Models",
author = "Qiao, Shanbao and
Liu, Xuebing and
Na, Seung-Hoon",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek"... | Among the recently emerged knowledge editing methods, in-context knowledge editing (IKE) has shown respectable abilities on knowledge editing in terms of generalization and specificity. Noting the promising advantages but unexplored issues of IKE, we propose **DistillMIKE** as a novel extension of IKE, i.e., editing **... | [
"Qiao, Shanbao",
"Liu, Xuebing",
"Na, Seung-Hoon"
] | {D}istill{MIKE}: Editing Distillation of Massive In-Context Knowledge Editing in Large Language Models | findings-acl.455 | Poster | 2310.10322v1 |
https://aclanthology.org/2024.findings-acl.456.bib | @inproceedings{xia-etal-2024-unlocking,
title = "Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding",
author = "Xia, Heming and
Yang, Zhe and
Dong, Qingxiu and
Wang, Peiyi and
Li, Yongqi and
Ge, Tao and
Liu, Tianyu an... | To mitigate the high inference latency stemming from autoregressive decoding in Large Language Models (LLMs), Speculative Decoding has emerged as a novel decoding paradigm for LLM inference. In each decoding step, this method first drafts several future tokens efficiently and then verifies them in parallel. Unlike auto... | [
"Xia, Heming",
"Yang, Zhe",
"Dong, Qingxiu",
"Wang, Peiyi",
"Li, Yongqi",
"Ge, Tao",
"Liu, Tianyu",
"Li, Wenjie",
"Sui, Zhifang"
] | Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding | findings-acl.456 | Poster | 2401.07851v3 |
https://aclanthology.org/2024.findings-acl.457.bib | @inproceedings{kim-etal-2024-hierarchy,
title = "Hierarchy-aware Biased Bound Margin Loss Function for Hierarchical Text Classification",
author = "Kim, Gibaeg and
Im, SangHun and
Oh, Heung-Seon",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Fin... | Hierarchical text classification (HTC) is a challenging problem with two key issues: utilizing structural information and mitigating label imbalance. Recently, the unit-based approach generating unit-based feature representations has outperformed the global approach focusing on a global feature representation. Neverthe... | [
"Kim, Gibaeg",
"Im, SangHun",
"Oh, Heung-Seon"
] | Hierarchy-aware Biased Bound Margin Loss Function for Hierarchical Text Classification | findings-acl.457 | Poster | 2306.09132v1 |
https://aclanthology.org/2024.findings-acl.458.bib | @inproceedings{chen-etal-2024-improving-retrieval,
title = "Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts",
author = "Chen, Zhuo and
Wang, Xinyu and
Jiang, Yong and
Xie, Pengjun and
Huang, Fei and
Tu, Kewei",
editor = "Ku, Lun-Wei a... | In the era of large language models, applying techniques such as Retrieval Augmented Generation can better address Open-Domain Question-Answering problems. Due to constraints including model sizes and computing resources, the length of context is often limited, and it becomes challenging to empower the model to cover o... | [
"Chen, Zhuo",
"Wang, Xinyu",
"Jiang, Yong",
"Xie, Pengjun",
"Huang, Fei",
"Tu, Kewei"
] | Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts | findings-acl.458 | Poster | 2310.03184v2 |
https://aclanthology.org/2024.findings-acl.459.bib | @inproceedings{randl-etal-2024-cicle,
title = "{CICL}e: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification",
author = "Randl, Korbinian and
Pavlopoulos, John and
Henriksson, Aron and
Lindgren, Tony",
editor = "Ku, Lun-Wei and
Martins, Andre and
... | Contaminated or adulterated food poses a substantial risk to human health. Given sets of labeled web texts for training, Machine Learning and Natural Language Processing can be applied to automatically detect such risks. We publish a dataset of 7,546 short texts describing public food recall announcements. Each text is... | [
"R",
"l, Korbinian",
"Pavlopoulos, John",
"Henriksson, Aron",
"Lindgren, Tony"
] | {CICL}e: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification | findings-acl.459 | Poster | 2403.11904v3 |
https://aclanthology.org/2024.findings-acl.460.bib | @inproceedings{liu-etal-2024-intactkv,
title = "{I}ntact{KV}: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact",
author = "Liu, Ruikang and
Bai, Haoli and
Lin, Haokun and
Li, Yuening and
Gao, Han and
Xu, Zhengzhuo and
Hou, Lu and
Yao, Ju... | Large language models (LLMs) excel in natural language processing but demand intensive computation. To mitigate this, various quantization methods have been explored, yet they compromise LLM performance. This paper unveils a previously overlooked type of outliers in LLMs. Such outliers are found to allocate most of the... | [
"Liu, Ruikang",
"Bai, Haoli",
"Lin, Haokun",
"Li, Yuening",
"Gao, Han",
"Xu, Zhengzhuo",
"Hou, Lu",
"Yao, Jun",
"Yuan, Chun"
] | {I}ntact{KV}: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact | findings-acl.460 | Poster | 2403.01241v2 |
https://aclanthology.org/2024.findings-acl.461.bib | @inproceedings{taniguchi-etal-2024-learning,
title = "Learning Adverbs with Spectral Mixture Kernels",
author = "Taniguchi, Tomoe and
Mochihashi, Daichi and
Kobayashi, Ichiro",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Associa... | For humans and robots to collaborate more in the real world, robots need to understand human intentions from the different manner of their behaviors. In our study, we focus on the meaning of adverbs which describe human motions. We propose a topic model, Hierarchical Dirichlet Process-Spectral Mixture Latent Dirichlet ... | [
"Taniguchi, Tomoe",
"Mochihashi, Daichi",
"Kobayashi, Ichiro"
] | Learning Adverbs with Spectral Mixture Kernels | findings-acl.461 | Poster | 2309.15086v1 |
https://aclanthology.org/2024.findings-acl.462.bib | @inproceedings{hou-etal-2024-e,
title = "{E}-{EVAL}: A Comprehensive {C}hinese K-12 Education Evaluation Benchmark for Large Language Models",
author = "Hou, Jinchang and
Ao, Chang and
Wu, Haihong and
Kong, Xiangtao and
Zheng, Zhigang and
Tang, Daijia and
Li, Chengming ... | The rapid development of Large Language Models (LLMs) has led to their increasing utilization in Chinese K-12 education. Despite the growing integration of LLMs and education, the absence of a dedicated benchmark for evaluating LLMs within this domain presents a pressing concern. Consequently, there is an urgent need f... | [
"Hou, Jinchang",
"Ao, Chang",
"Wu, Haihong",
"Kong, Xiangtao",
"Zheng, Zhigang",
"Tang, Daijia",
"Li, Chengming",
"Hu, Xiping",
"Xu, Ruifeng",
"Ni, Shiwen",
"Yang, Min"
] | {E}-{EVAL}: A Comprehensive {C}hinese K-12 Education Evaluation Benchmark for Large Language Models | findings-acl.462 | Poster | 2401.15927v1 |
https://aclanthology.org/2024.findings-acl.463.bib | @inproceedings{meng-etal-2024-chartassistant,
title = "{C}hart{A}ssistant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning",
author = "Meng, Fanqing and
Shao, Wenqi and
Lu, Quanfeng and
Gao, Peng and
Zhang, Kaipeng and
... | Charts play a vital role in data visualization, understanding data patterns, and informed decision-making. However, their unique combination of graphical elements (e.g., bars, lines) and textual components (e.g., labels, legends) poses challenges for general-purpose multimodal models. While vision-language models train... | [
"Meng, Fanqing",
"Shao, Wenqi",
"Lu, Quanfeng",
"Gao, Peng",
"Zhang, Kaipeng",
"Qiao, Yu",
"Luo, Ping"
] | {C}hart{A}ssistant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning | findings-acl.463 | Poster | 2401.02384v3 |
https://aclanthology.org/2024.findings-acl.464.bib | @inproceedings{li-etal-2024-teaching,
title = "Teaching Small Language Models to Reason for Knowledge-Intensive Multi-Hop Question Answering",
author = "Li, Xiang and
He, Shizhu and
Lei, Fangyu and
JunYang, JunYang and
Su, Tianhuang and
Liu, Kang and
Zhao, Jun",
edi... | Large Language Models (LLMs) can teach small language models (SLMs) to solve complex reasoning tasks (e.g., mathematical question answering) by Chain-of-thought Distillation (CoTD). Specifically, CoTD fine-tunes SLMs by utilizing rationales generated from LLMs such as ChatGPT. However, CoTD has certain limitations that... | [
"Li, Xiang",
"He, Shizhu",
"Lei, Fangyu",
"JunYang, JunYang",
"Su, Tianhuang",
"Liu, Kang",
"Zhao, Jun"
] | Teaching Small Language Models to Reason for Knowledge-Intensive Multi-Hop Question Answering | findings-acl.464 | Poster | 2305.03453v4 |
https://aclanthology.org/2024.findings-acl.465.bib | @inproceedings{lai-etal-2024-alarm,
title = "{AL}a{RM}: Align Language Models via Hierarchical Rewards Modeling",
author = "Lai, Yuhang and
Wang, Siyuan and
Liu, Shujun and
Huang, Xuanjing and
Wei, Zhongyu",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Viv... | We introduce ALaRM, the first framework modeling hierarchical rewards in reinforcement learning from human feedback (RLHF), which is designed to enhance the alignment of large language models (LLMs) with human preferences. The framework addresses the limitations of current alignment approaches, which often struggle wit... | [
"Lai, Yuhang",
"Wang, Siyuan",
"Liu, Shujun",
"Huang, Xuanjing",
"Wei, Zhongyu"
] | {AL}a{RM}: Align Language Models via Hierarchical Rewards Modeling | findings-acl.465 | Poster | 2403.06754v2 |
https://aclanthology.org/2024.findings-acl.466.bib | @inproceedings{liu-etal-2024-lstprompt,
title = "{LSTP}rompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting",
author = "Liu, Haoxin and
Zhao, Zhiyuan and
Wang, Jindong and
Kamarthi, Harshavardhan and
Prakash, B. Aditya",
editor = "Ku, Lun... | Time-series forecasting (TSF) finds broad applications in real-world scenarios. Prompting off-the-shelf Large Language Models (LLMs) demonstrates strong zero-shot TSF capabilities while preserving computational efficiency. However, existing prompting methods oversimplify TSF as language next-token predictions, overlook... | [
"Liu, Haoxin",
"Zhao, Zhiyuan",
"Wang, Jindong",
"Kamarthi, Harshavardhan",
"Prakash, B. Aditya"
] | {LSTP}rompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting | findings-acl.466 | Poster | 2210.08964v5 |
https://aclanthology.org/2024.findings-acl.467.bib | @inproceedings{lu-etal-2024-mitigating,
title = "Mitigating Boundary Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Models",
author = "Lu, Zhenyi and
Tian, Jie and
Wei, Wei and
Qu, Xiaoye and
Cheng, Yu and
Xie, Wenfeng and
Chen, Dangyan... | Text classification is a crucial task encountered frequently in practical scenarios, yet it is still under-explored in the era of large language models (LLMs). This study shows that LLMs are vulnerable to changes in the number and arrangement of options in text classification. Our extensive empirical analyses reveal th... | [
"Lu, Zhenyi",
"Tian, Jie",
"Wei, Wei",
"Qu, Xiaoye",
"Cheng, Yu",
"Xie, Wenfeng",
"Chen, Dangyang"
] | Mitigating Boundary Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Models | findings-acl.467 | Poster | 2406.07001v1 |
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