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.acl-long.201.bib | @inproceedings{gaido-etal-2024-sbaam,
title = "{SBAAM}! Eliminating Transcript Dependency in Automatic Subtitling",
author = "Gaido, Marco and
Papi, Sara and
Negri, Matteo and
Cettolo, Mauro and
Bentivogli, Luisa",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikum... | Subtitling plays a crucial role in enhancing the accessibility of audiovisual content and encompasses three primary subtasks: translating spoken dialogue, segmenting translations into concise textual units, and estimating timestamps that govern their on-screen duration. Past attempts to automate this process rely, to v... | [
"Gaido, Marco",
"Papi, Sara",
"Negri, Matteo",
"Cettolo, Mauro",
"Bentivogli, Luisa"
] | {SBAAM}! Eliminating Transcript Dependency in Automatic Subtitling | acl-long.201 | Poster | 2002.10829v1 |
https://aclanthology.org/2024.acl-long.202.bib | @inproceedings{papi-etal-2024-streamatt,
title = "{S}tream{A}tt: Direct Streaming Speech-to-Text Translation with Attention-based Audio History Selection",
author = "Papi, Sara and
Gaido, Marco and
Negri, Matteo and
Bentivogli, Luisa",
editor = "Ku, Lun-Wei and
Martins, Andre an... | Streaming speech-to-text translation (StreamST) is the task of automatically translating speech while incrementally receiving an audio stream. Unlike simultaneous ST (SimulST), which deals with pre-segmented speech, StreamST faces the challenges of handling continuous and unbounded audio streams. This requires addition... | [
"Papi, Sara",
"Gaido, Marco",
"Negri, Matteo",
"Bentivogli, Luisa"
] | {S}tream{A}tt: Direct Streaming Speech-to-Text Translation with Attention-based Audio History Selection | acl-long.202 | Poster | 2406.06097v1 |
https://aclanthology.org/2024.acl-long.203.bib | @inproceedings{zhang-etal-2024-arl2,
title = "{ARL}2: Aligning Retrievers with Black-box Large Language Models via Self-guided Adaptive Relevance Labeling",
author = "Zhang, LingXi and
Yu, Yue and
Wang, Kuan and
Zhang, Chao",
editor = "Ku, Lun-Wei and
Martins, Andre and
Sr... | Retrieval-augmented generation enhances large language models (LLMs) by incorporating relevant information from external knowledge sources. This enables LLMs to adapt to specific domains and mitigate hallucinations in knowledge-intensive tasks. However, existing retrievers are often misaligned with LLMs due to separate... | [
"Zhang, LingXi",
"Yu, Yue",
"Wang, Kuan",
"Zhang, Chao"
] | {ARL}2: Aligning Retrievers with Black-box Large Language Models via Self-guided Adaptive Relevance Labeling | acl-long.203 | Poster | 2402.13542v2 |
https://aclanthology.org/2024.acl-long.204.bib | @inproceedings{bang-etal-2024-crayon,
title = "Crayon: Customized On-Device {LLM} via Instant Adapter Blending and Edge-Server Hybrid Inference",
author = "Bang, Jihwan and
Lee, Juntae and
Shim, Kyuhong and
Yang, Seunghan and
Chang, Simyung",
editor = "Ku, Lun-Wei and
Mart... | The customization of large language models (LLMs) for user-specified tasks gets important. However, maintaining all the customized LLMs on cloud servers incurs substantial memory and computational overheads, and uploading user data can also lead to privacy concerns. On-device LLMs can offer a promising solution by miti... | [
"Bang, Jihwan",
"Lee, Juntae",
"Shim, Kyuhong",
"Yang, Seunghan",
"Chang, Simyung"
] | Crayon: Customized On-Device {LLM} via Instant Adapter Blending and Edge-Server Hybrid Inference | acl-long.204 | Poster | 2406.07007v1 |
https://aclanthology.org/2024.acl-long.205.bib | @inproceedings{lee-etal-2024-fleur,
title = "{FLEUR}: An Explainable Reference-Free Evaluation Metric for Image Captioning Using a Large Multimodal Model",
author = "Lee, Yebin and
Park, Imseong and
Kang, Myungjoo",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
... | Most existing image captioning evaluation metrics focus on assigning a single numerical score to a caption by comparing it with reference captions. However, these methods do not provide an explanation for the assigned score. Moreover, reference captions are expensive to acquire. In this paper, we propose FLEUR, an expl... | [
"Lee, Yebin",
"Park, Imseong",
"Kang, Myungjoo"
] | {FLEUR}: An Explainable Reference-Free Evaluation Metric for Image Captioning Using a Large Multimodal Model | acl-long.205 | Poster | 2406.06004v1 |
https://aclanthology.org/2024.acl-long.206.bib | @inproceedings{wang-etal-2024-mentalmanip,
title = "{M}ental{M}anip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations",
author = "Wang, Yuxin and
Yang, Ivory and
Hassanpour, Saeed and
Vosoughi, Soroush",
editor = "Ku, Lun-Wei and
Martins, Andre and
... | Mental manipulation, a significant form of abuse in interpersonal conversations, presents a challenge to identify due to its context-dependent and often subtle nature. The detection of manipulative language is essential for protecting potential victims, yet the field of Natural Language Processing (NLP) currently faces... | [
"Wang, Yuxin",
"Yang, Ivory",
"Hassanpour, Saeed",
"Vosoughi, Soroush"
] | {M}ental{M}anip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations | acl-long.206 | Oral | 2405.16584v1 |
https://aclanthology.org/2024.acl-long.207.bib | @inproceedings{dai-etal-2024-mpcoder,
title = "{MPC}oder: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learning",
author = "Dai, Zhenlong and
Yao, Chang and
Han, WenKang and
Yuanying, Yuanying and
Gao, Zhipeng and
Chen, Jingyuan",
... | Large Language Models (LLMs) have demonstrated great potential for assisting developers in their daily development. However, most research focuses on generating correct code, how to use LLMs to generate personalized code has seldom been investigated. To bridge this gap, we proposed MPCoder (Multi-user Personalized Code... | [
"Dai, Zhenlong",
"Yao, Chang",
"Han, WenKang",
"Yuanying, Yuanying",
"Gao, Zhipeng",
"Chen, Jingyuan"
] | {MPC}oder: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learning | acl-long.207 | Poster | 2406.17255v1 |
https://aclanthology.org/2024.acl-long.208.bib | @inproceedings{patel-etal-2024-datadreamer,
title = "{D}ata{D}reamer: A Tool for Synthetic Data Generation and Reproducible {LLM} Workflows",
author = "Patel, Ajay and
Raffel, Colin and
Callison-Burch, Chris",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
boo... | Large language models (LLMs) have become a dominant and important tool for NLP researchers in a wide range of tasks. Today, many researchers use LLMs in synthetic data generation, task evaluation, fine-tuning, distillation, and other model-in-the-loop research workflows. However, challenges arise when using these model... | [
"Patel, Ajay",
"Raffel, Colin",
"Callison-Burch, Chris"
] | {D}ata{D}reamer: A Tool for Synthetic Data Generation and Reproducible {LLM} Workflows | acl-long.208 | Poster | 2402.10379v2 |
https://aclanthology.org/2024.acl-long.209.bib | @inproceedings{shao-etal-2024-understanding,
title = "Understanding and Addressing the Under-Translation Problem from the Perspective of Decoding Objective",
author = "Shao, Chenze and
Meng, Fandong and
Zeng, Jiali and
Zhou, Jie",
editor = "Ku, Lun-Wei and
Martins, Andre and
... | Neural Machine Translation (NMT) has made remarkable progress over the past years. However, under-translation and over-translation remain two challenging problems in state-of-the-art NMT systems. In this work, we conduct an in-depth analysis on the underlying cause of under-translation in NMT, providing an explanation ... | [
"Shao, Chenze",
"Meng, F",
"ong",
"Zeng, Jiali",
"Zhou, Jie"
] | Understanding and Addressing the Under-Translation Problem from the Perspective of Decoding Objective | acl-long.209 | Poster | 2405.18922v1 |
https://aclanthology.org/2024.acl-long.210.bib | @inproceedings{liu-etal-2024-identifying,
title = "Identifying while Learning for Document Event Causality Identification",
author = "Liu, Cheng and
Xiang, Wei and
Wang, Bang",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd... | Event Causality Identification (ECI) aims to detect whether there exists a causal relation between two events in a document. Existing studies adopt a kind of *identifying after learning* paradigm, where events{'} representations are first learned and then used for the identification. Furthermore, they mainly focus on t... | [
"Liu, Cheng",
"Xiang, Wei",
"Wang, Bang"
] | Identifying while Learning for Document Event Causality Identification | acl-long.210 | Poster | 2405.20608v1 |
https://aclanthology.org/2024.acl-long.211.bib | @inproceedings{he-etal-2024-olympiadbench,
title = "{O}lympiad{B}ench: A Challenging Benchmark for Promoting {AGI} with Olympiad-Level Bilingual Multimodal Scientific Problems",
author = "He, Chaoqun and
Luo, Renjie and
Bai, Yuzhuo and
Hu, Shengding and
Thai, Zhen and
Shen, Ju... | Recent advancements have seen Large Language Models (LLMs) and Large Multimodal Models (LMMs) surpassing general human capabilities in various tasks, approaching the proficiency level of human experts across multiple domains. With traditional benchmarks becoming less challenging for these models, new rigorous challenge... | [
"He, Chaoqun",
"Luo, Renjie",
"Bai, Yuzhuo",
"Hu, Shengding",
"Thai, Zhen",
"Shen, Junhao",
"Hu, Jinyi",
"Han, Xu",
"Huang, Yujie",
"Zhang, Yuxiang",
"Liu, Jie",
"Qi, Lei",
"Liu, Zhiyuan",
"Sun, Maosong"
] | {O}lympiad{B}ench: A Challenging Benchmark for Promoting {AGI} with Olympiad-Level Bilingual Multimodal Scientific Problems | acl-long.211 | Poster | 2402.14008v2 |
https://aclanthology.org/2024.acl-long.212.bib | @inproceedings{xue-etal-2024-insert,
title = "Insert or Attach: Taxonomy Completion via Box Embedding",
author = "Xue, Wei and
Shen, Yongliang and
Ren, Wenqi and
Guo, Jietian and
Pu, Shiliang and
Lu, Weiming",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srik... | Taxonomy completion, enriching existing taxonomies by inserting new concepts as parents or attaching them as children, has gained significant interest. Previous approaches embed concepts as vectors in Euclidean space, which makes it difficult to model asymmetric relations in taxonomy. In addition, they introduce pseudo... | [
"Xue, Wei",
"Shen, Yongliang",
"Ren, Wenqi",
"Guo, Jietian",
"Pu, Shiliang",
"Lu, Weiming"
] | Insert or Attach: Taxonomy Completion via Box Embedding | acl-long.212 | Poster | 2305.11004v4 |
https://aclanthology.org/2024.acl-long.213.bib | @inproceedings{lee-etal-2024-semiparametric,
title = "Semiparametric Token-Sequence Co-Supervision",
author = "Lee, Hyunji and
Kim, Doyoung and
Jun, Jihoon and
Joo, Se June and
Jang, Joel and
On, Kyoung-Woon and
Seo, Minjoon",
editor = "Ku, Lun-Wei and
Martin... | In this work, we introduce a semiparametric token-sequence co-supervision training method. It trains a language model by simultaneously leveraging supervision from the traditional next token prediction loss which is calculated over the parametric token embedding space and the next sequence prediction loss which is calc... | [
"Lee, Hyunji",
"Kim, Doyoung",
"Jun, Jihoon",
"Joo, Se June",
"Jang, Joel",
"On, Kyoung-Woon",
"Seo, Minjoon"
] | Semiparametric Token-Sequence Co-Supervision | acl-long.213 | Poster | 2402.15505v1 |
https://aclanthology.org/2024.acl-long.214.bib | @inproceedings{guo-etal-2024-instruction,
title = "Instruction Fusion: Advancing Prompt Evolution through Hybridization",
author = "Guo, Weidong and
Yang, Jiuding and
Yang, Kaitong and
Li, Xiangyang and
Rao, Zhuwei and
Xu, Yu and
Niu, Di",
editor = "Ku, Lun-Wei and... | The fine-tuning of Large Language Models (LLMs) specialized in code generation has seen notable advancements through the use of open-domain coding queries. Despite the successes, existing methodologies like Evol-Instruct encounter performance limitations, impeding further enhancements in code generation tasks. This pap... | [
"Guo, Weidong",
"Yang, Jiuding",
"Yang, Kaitong",
"Li, Xiangyang",
"Rao, Zhuwei",
"Xu, Yu",
"Niu, Di"
] | Instruction Fusion: Advancing Prompt Evolution through Hybridization | acl-long.214 | Poster | 2312.15692v4 |
https://aclanthology.org/2024.acl-long.215.bib | @inproceedings{zhang-etal-2024-timearena,
title = "{T}ime{A}rena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation",
author = "Zhang, Yikai and
Yuan, Siyu and
Hu, Caiyu and
Richardson, Kyle and
Xiao, Yanghua and
Chen, Jiangjie",
editor = "Ku, Lun-W... | Despite remarkable advancements in emulating human-like behavior through Large Language Models (LLMs), current textual simulations do not adequately address the notion of time. To this end, we introduce TimeArena, a novel textual simulated environment that incorporates complex temporal dynamics and constraints that bet... | [
"Zhang, Yikai",
"Yuan, Siyu",
"Hu, Caiyu",
"Richardson, Kyle",
"Xiao, Yanghua",
"Chen, Jiangjie"
] | {T}ime{A}rena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation | acl-long.215 | Poster | 2402.05733v1 |
https://aclanthology.org/2024.acl-long.216.bib | @inproceedings{zeng-etal-2024-exploring,
title = "Exploring Memorization in Fine-tuned Language Models",
author = "Zeng, Shenglai and
Li, Yaxin and
Ren, Jie and
Liu, Yiding and
Xu, Han and
He, Pengfei and
Xing, Yue and
Wang, Shuaiqiang and
Tang, Jiliang a... | Large language models (LLMs) have shown great capabilities in various tasks but also exhibited memorization of training data, raising tremendous privacy and copyright concerns. While prior works have studied memorization during pre-training, the exploration of memorization during fine-tuning is rather limited. Compared... | [
"Zeng, Shenglai",
"Li, Yaxin",
"Ren, Jie",
"Liu, Yiding",
"Xu, Han",
"He, Pengfei",
"Xing, Yue",
"Wang, Shuaiqiang",
"Tang, Jiliang",
"Yin, Dawei"
] | Exploring Memorization in Fine-tuned Language Models | acl-long.216 | Poster | 2305.04673v2 |
https://aclanthology.org/2024.acl-long.217.bib | @inproceedings{zhang-etal-2024-towards-real,
title = "Towards Real-world Scenario: Imbalanced New Intent Discovery",
author = "Zhang, Shun and
Chaoran, Yan and
Yang, Jian and
Liu, Jiaheng and
Mo, Ying and
Bai, Jiaqi and
Li, Tongliang and
Li, Zhoujun",
editor ... | New Intent Discovery (NID) aims at detecting known and previously undefined categories of user intent by utilizing limited labeled and massive unlabeled data. Most prior works often operate under the unrealistic assumption that the distribution of both familiar and new intent classes is uniform, overlooking the skewed ... | [
"Zhang, Shun",
"Chaoran, Yan",
"Yang, Jian",
"Liu, Jiaheng",
"Mo, Ying",
"Bai, Jiaqi",
"Li, Tongliang",
"Li, Zhoujun"
] | Towards Real-world Scenario: Imbalanced New Intent Discovery | acl-long.217 | Poster | 2310.10184v1 |
https://aclanthology.org/2024.acl-long.218.bib | @inproceedings{wang-etal-2024-m4gt,
title = "{M}4{GT}-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection",
author = "Wang, Yuxia and
Mansurov, Jonibek and
Ivanov, Petar and
Su, Jinyan and
Shelmanov, Artem and
Tsvigun, Akim and
Mohammed Afzal, Osa... | The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels. This raises legitimate concerns about its potential misuse and societal implications. The need to identify and differentiate such content from genuine human-generated text is critical i... | [
"Wang, Yuxia",
"Mansurov, Jonibek",
"Ivanov, Petar",
"Su, Jinyan",
"Shelmanov, Artem",
"Tsvigun, Akim",
"Mohammed Afzal, Osama",
"Mahmoud, Tarek",
"Puccetti, Giovanni",
"Arnold, Thomas",
"Aji, Alham",
"Habash, Nizar",
"Gurevych, Iryna",
"Nakov, Preslav"
] | {M}4{GT}-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection | acl-long.218 | Poster | 2010.08660v4 |
https://aclanthology.org/2024.acl-long.219.bib | @inproceedings{wang-etal-2024-instruct,
title = "Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue",
author = "Wang, Jian and
Leong, Chak Tou and
Wang, Jiashuo and
Lin, Dongding and
Li, Wenjie and
Wei, Xiaoyong",
editor = "Ku, ... | Tuning language models for dialogue generation has been a prevalent paradigm for building capable dialogue agents. Yet, traditional tuning narrowly views dialogue generation as resembling other language generation tasks, ignoring the role disparities between two speakers and the multi-round interactive process that dia... | [
"Wang, Jian",
"Leong, Chak Tou",
"Wang, Jiashuo",
"Lin, Dongding",
"Li, Wenjie",
"Wei, Xiaoyong"
] | Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue | acl-long.219 | Poster | 2402.06967v2 |
https://aclanthology.org/2024.acl-long.220.bib | @inproceedings{he-etal-2024-softdedup,
title = "{S}oft{D}edup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training",
author = "He, Nan and
Xiong, Weichen and
Liu, Hanwen and
Liao, Yi and
Ding, Lei and
Zhang, Kai and
Tang, Guohua and
H... | The effectiveness of large language models (LLMs) is often hindered by duplicated data in their extensive pre-training datasets. Current approaches primarily focus on detecting and removing duplicates, which risks the loss of valuable information and neglects the varying degrees of duplication. To address this, we prop... | [
"He, Nan",
"Xiong, Weichen",
"Liu, Hanwen",
"Liao, Yi",
"Ding, Lei",
"Zhang, Kai",
"Tang, Guohua",
"Han, Xiao",
"Wei, Yang"
] | {S}oft{D}edup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training | acl-long.220 | Poster | 2407.06654v1 |
https://aclanthology.org/2024.acl-long.221.bib | @inproceedings{bian-etal-2024-rule,
title = "Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models?",
author = "Bian, Ning and
Han, Xianpei and
Lin, Hongyu and
Lu, Yaojie and
He, Ben and
Sun, Le",
editor = "Ku, Lun-Wei and
Ma... | Building machines with commonsense has been a longstanding challenge in NLP due to the reporting bias of commonsense rules and the exposure bias of rule-based commonsense reasoning. In contrast, humans convey and pass down commonsense implicitly through stories. This paper investigates the inherent commonsense ability ... | [
"Bian, Ning",
"Han, Xianpei",
"Lin, Hongyu",
"Lu, Yaojie",
"He, Ben",
"Sun, Le"
] | Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models? | acl-long.221 | Poster | 2402.14355v2 |
https://aclanthology.org/2024.acl-long.222.bib | @inproceedings{tan-etal-2024-learning,
title = "Learning Global Controller in Latent Space for Parameter-Efficient Fine-Tuning",
author = "Tan, Zeqi and
Shen, Yongliang and
Cheng, Xiaoxia and
Zong, Chang and
Zhang, Wenqi and
Shao, Jian and
Lu, Weiming and
Zhuang,... | While large language models (LLMs) have showcased remarkable prowess in various natural language processing tasks, their training costs are exorbitant. Consequently, a plethora of parameter-efficient fine-tuning methods have emerged to tailor large models for downstream tasks, including low-rank training. Recent approa... | [
"Tan, Zeqi",
"Shen, Yongliang",
"Cheng, Xiaoxia",
"Zong, Chang",
"Zhang, Wenqi",
"Shao, Jian",
"Lu, Weiming",
"Zhuang, Yueting"
] | Learning Global Controller in Latent Space for Parameter-Efficient Fine-Tuning | acl-long.222 | Poster | 2306.11378v1 |
https://aclanthology.org/2024.acl-long.223.bib | @inproceedings{chen-etal-2024-camml,
title = "{C}a{MML}: Context-Aware Multimodal Learner for Large Models",
author = "Chen, Yixin and
Zhang, Shuai and
Han, Boran and
He, Tong and
Li, Bo",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle ... | In this work, we introduce Context-Aware MultiModal Learner (CaMML), for tuning large multimodal models (LMMs). CaMML, a lightweight module, is crafted to seamlessly integrate multimodal contextual samples into large models, thereby empowering the model to derive knowledge from analogous, domain-specific, up-to-date in... | [
"Chen, Yixin",
"Zhang, Shuai",
"Han, Boran",
"He, Tong",
"Li, Bo"
] | {C}a{MML}: Context-Aware Multimodal Learner for Large Models | acl-long.223 | Oral | 2401.03149v3 |
https://aclanthology.org/2024.acl-long.224.bib | @inproceedings{wang-etal-2024-maven,
title = "{MAVEN}-{ARG}: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation",
author = "Wang, Xiaozhi and
Peng, Hao and
Guan, Yong and
Zeng, Kaisheng and
Chen, Jianhui and
Hou, Lei and
Han, ... | Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-event relationships. However, due to the annotation challenges brought by task complexity, a large-scale dataset covering the full process of e... | [
"Wang, Xiaozhi",
"Peng, Hao",
"Guan, Yong",
"Zeng, Kaisheng",
"Chen, Jianhui",
"Hou, Lei",
"Han, Xu",
"Lin, Yankai",
"Liu, Zhiyuan",
"Xie, Ruobing",
"Zhou, Jie",
"Li, Juanzi"
] | {MAVEN}-{ARG}: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation | acl-long.224 | Oral | 2311.09105v2 |
https://aclanthology.org/2024.acl-long.225.bib | @inproceedings{fan-etal-2024-nphardeval,
title = "{NPH}ard{E}val: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes",
author = "Fan, Lizhou and
Hua, Wenyue and
Li, Lingyao and
Ling, Haoyang and
Zhang, Yongfeng",
editor = "Ku, Lun-Wei and
... | Complex reasoning ability is one of the most important features of Large Language Models (LLMs). Numerous benchmarks have been established to assess the reasoning abilities of LLMs. However, they are inadequate in offering a rigorous evaluation and prone to the risk of overfitting, as these publicly accessible and stat... | [
"Fan, Lizhou",
"Hua, Wenyue",
"Li, Lingyao",
"Ling, Haoyang",
"Zhang, Yongfeng"
] | {NPH}ard{E}val: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes | acl-long.225 | Poster | 2312.14890v4 |
https://aclanthology.org/2024.acl-long.226.bib | @inproceedings{he-etal-2024-watermarks,
title = "Can Watermarks Survive Translation? On the Cross-lingual Consistency of Text Watermark for Large Language Models",
author = "He, Zhiwei and
Zhou, Binglin and
Hao, Hongkun and
Liu, Aiwei and
Wang, Xing and
Tu, Zhaopeng and
... | Text watermarking technology aims to tag and identify content produced by large language models (LLMs) to prevent misuse. In this study, we introduce the concept of cross-lingual consistency in text watermarking, which assesses the ability of text watermarks to maintain their effectiveness after being translated into o... | [
"He, Zhiwei",
"Zhou, Binglin",
"Hao, Hongkun",
"Liu, Aiwei",
"Wang, Xing",
"Tu, Zhaopeng",
"Zhang, Zhuosheng",
"Wang, Rui"
] | Can Watermarks Survive Translation? On the Cross-lingual Consistency of Text Watermark for Large Language Models | acl-long.226 | Oral | 2402.14007v2 |
https://aclanthology.org/2024.acl-long.227.bib | @inproceedings{chaszczewicz-etal-2024-multi,
title = "Multi-Level Feedback Generation with Large Language Models for Empowering Novice Peer Counselors",
author = "Chaszczewicz, Alicja and
Shah, Raj and
Louie, Ryan and
Arnow, Bruce and
Kraut, Robert and
Yang, Diyi",
editor ... | Realistic practice and tailored feedback are key processes for training peer counselors with clinical skills. However, existing mechanisms of providing feedback largely rely on human supervision. Peer counselors often lack mechanisms to receive detailed feedback from experienced mentors, making it difficult for them to... | [
"Chaszczewicz, Alicja",
"Shah, Raj",
"Louie, Ryan",
"Arnow, Bruce",
"Kraut, Robert",
"Yang, Diyi"
] | Multi-Level Feedback Generation with Large Language Models for Empowering Novice Peer Counselors | acl-long.227 | Poster | 2403.15482v1 |
https://aclanthology.org/2024.acl-long.228.bib | @inproceedings{shankar-etal-2024-context,
title = "In-context Mixing ({ICM}): Code-mixed Prompts for Multilingual {LLM}s",
author = "Shankar, Bhavani and
Jyothi, Preethi and
Bhattacharyya, Pushpak",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "P... | We introduce a simple and effective prompting technique called in-context mixing (ICM) for effective in-context learning (ICL) with multilingual large language models (MLLMs). With ICM, we modify the few-shot examples within ICL prompts to be intra-sententially code-mixed by randomly swapping content words in the targe... | [
"Shankar, Bhavani",
"Jyothi, Preethi",
"Bhattacharyya, Pushpak"
] | In-context Mixing ({ICM}): Code-mixed Prompts for Multilingual {LLM}s | acl-long.228 | Poster | null |
https://aclanthology.org/2024.acl-long.229.bib | @inproceedings{zhang-etal-2024-respond,
title = "Respond in my Language: Mitigating Language Inconsistency in Response Generation based on Large Language Models",
author = "Zhang, Liang and
Jin, Qin and
Huang, Haoyang and
Zhang, Dongdong and
Wei, Furu",
editor = "Ku, Lun-Wei and... | Large Language Models (LLMs) show strong instruction understanding ability across multiple languages. However, they are easily biased towards English in instruction tuning, and generate English responses even given non-English instructions. In this paper, we investigate the language inconsistent generation problem in m... | [
"Zhang, Liang",
"Jin, Qin",
"Huang, Haoyang",
"Zhang, Dongdong",
"Wei, Furu"
] | Respond in my Language: Mitigating Language Inconsistency in Response Generation based on Large Language Models | acl-long.229 | Poster | 2309.02654v3 |
https://aclanthology.org/2024.acl-long.230.bib | @inproceedings{huang-etal-2024-transferable,
title = "Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries",
author = "Huang, Yu-Hsiang and
Tsai, Yuche and
Hsiao, Hsiang and
Lin, Hong-Yi and
Lin, Shou-De",
editor = "Ku, Lun-We... | This study investigates the privacy risks associated with text embeddings, focusing on the scenario where attackers cannot access the original embedding model. Contrary to previous research requiring direct model access, we explore a more realistic threat model by developing a transfer attack method. This approach uses... | [
"Huang, Yu-Hsiang",
"Tsai, Yuche",
"Hsiao, Hsiang",
"Lin, Hong-Yi",
"Lin, Shou-De"
] | Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries | acl-long.230 | Poster | 2406.10280v1 |
https://aclanthology.org/2024.acl-long.231.bib | @inproceedings{liao-etal-2024-enhancing,
title = "Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding",
author = "Liao, Kuo and
Li, Shuang and
Zhao, Meng and
Liu, Liqun and
Xue, Mengge and
Hu, Zhenyu and
Han, Honglin and
... | Recent strides in large language models (LLMs) have yielded remarkable performance, leveraging reinforcement learning from human feedback (RLHF) to significantly enhance generation and alignment capabilities. However, RLHF encounters numerous challenges, including the objective mismatch issue, leading to suboptimal per... | [
"Liao, Kuo",
"Li, Shuang",
"Zhao, Meng",
"Liu, Liqun",
"Xue, Mengge",
"Hu, Zhenyu",
"Han, Honglin",
"Yin, Chengguo"
] | Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding | acl-long.231 | Poster | 2405.19763v1 |
https://aclanthology.org/2024.acl-long.232.bib | @inproceedings{ying-etal-2024-intuitive,
title = "Intuitive or Dependent? Investigating {LLM}s{'} Behavior Style to Conflicting Prompts",
author = "Ying, Jiahao and
Cao, Yixin and
Xiong, Kai and
Cui, Long and
He, Yidong and
Liu, Yongbin",
editor = "Ku, Lun-Wei and
M... | This study investigates the behaviors of Large Language Models (LLMs) when faced with conflicting prompts versus their internal memory. This will not only help to understand LLMs{'} decision mechanism but also benefit real-world applications, such as retrieval-augmented generation (RAG).Drawing on cognitive theory, we ... | [
"Ying, Jiahao",
"Cao, Yixin",
"Xiong, Kai",
"Cui, Long",
"He, Yidong",
"Liu, Yongbin"
] | Intuitive or Dependent? Investigating {LLM}s{'} Behavior Style to Conflicting Prompts | acl-long.232 | Poster | 2309.17415v3 |
https://aclanthology.org/2024.acl-long.233.bib | @inproceedings{zhu-etal-2024-coca,
title = "{C}o{CA}: Fusing Position Embedding with Collinear Constrained Attention in Transformers for Long Context Window Extending",
author = "Zhu, Shiyi and
Ye, Jing and
Jiang, Wei and
Xue, Siqiao and
Zhang, Qi and
Wu, Yifan and
Li, ... | Self-attention and position embedding are two crucial modules in transformer-based Large Language Models (LLMs). However, the potential relationship between them is far from well studied, especially for long context window extending. In fact, anomalous behaviors that hinder long context extrapolation exist between Rota... | [
"Zhu, Shiyi",
"Ye, Jing",
"Jiang, Wei",
"Xue, Siqiao",
"Zhang, Qi",
"Wu, Yifan",
"Li, Jianguo"
] | {C}o{CA}: Fusing Position Embedding with Collinear Constrained Attention in Transformers for Long Context Window Extending | acl-long.233 | Poster | 2309.08646v3 |
https://aclanthology.org/2024.acl-long.234.bib | @inproceedings{trienes-etal-2024-infolossqa,
title = "{I}nfo{L}oss{QA}: Characterizing and Recovering Information Loss in Text Simplification",
author = {Trienes, Jan and
Joseph, Sebastian and
Schl{\"o}tterer, J{\"o}rg and
Seifert, Christin and
Lo, Kyle and
Xu, Wei and
... | Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness. This work proposes InfoLossQA, a framework to characterize and recover simplification-induced information loss in form of question-and-answer (QA) pairs. Building on the theory of Qu... | [
"Trienes, Jan",
"Joseph, Sebastian",
"Schl{\\\"o}tterer, J{\\\"o}rg",
"Seifert, Christin",
"Lo, Kyle",
"Xu, Wei",
"Wallace, Byron",
"Li, Junyi Jessy"
] | {I}nfo{L}oss{QA}: Characterizing and Recovering Information Loss in Text Simplification | acl-long.234 | Poster | 2401.16475v2 |
https://aclanthology.org/2024.acl-long.235.bib | @inproceedings{zhang-etal-2024-cogenesis,
title = "{C}o{G}enesis: A Framework Collaborating Large and Small Language Models for Secure Context-Aware Instruction Following",
author = "Zhang, Kaiyan and
Wang, Jianyu and
Hua, Ermo and
Qi, Biqing and
Ding, Ning and
Zhou, Bowen",
... | With the advancement of language models (LMs), their exposure to private data is increasingly inevitable, and their deployment (especially for smaller ones) on personal devices, such as PCs and smartphones, has become a prevailing trend. In contexts laden with user information, enabling models to both safeguard user pr... | [
"Zhang, Kaiyan",
"Wang, Jianyu",
"Hua, Ermo",
"Qi, Biqing",
"Ding, Ning",
"Zhou, Bowen"
] | {C}o{G}enesis: A Framework Collaborating Large and Small Language Models for Secure Context-Aware Instruction Following | acl-long.235 | Poster | 2311.18215v1 |
https://aclanthology.org/2024.acl-long.236.bib | @inproceedings{wang-etal-2024-dapr,
title = "{DAPR}: A Benchmark on Document-Aware Passage Retrieval",
author = "Wang, Kexin and
Reimers, Nils and
Gurevych, Iryna",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meet... | The work of neural retrieval so far focuses on ranking short texts and is challenged with long documents. There are many cases where the users want to find a relevant passage within a long document from a huge corpus, e.g. Wikipedia articles, research papers, etc. We propose and name this task \textit{Document-Aware Pa... | [
"Wang, Kexin",
"Reimers, Nils",
"Gurevych, Iryna"
] | {DAPR}: A Benchmark on Document-Aware Passage Retrieval | acl-long.236 | Poster | 1805.03797v1 |
https://aclanthology.org/2024.acl-long.237.bib | @inproceedings{xue-etal-2024-strengthened,
title = "Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors",
author = "Xue, Mengge and
Hu, Zhenyu and
Liu, Liqun and
Liao, Kuo and
Li, Shuang and
Han, Honglin and
Zhao, Meng and
Y... | Multiple-Choice Questions (MCQs) constitute a critical area of research in the study of Large Language Models (LLMs). Previous works have investigated the selection bias problem in MCQs within few-shot scenarios, in which the LLM{'}s performance may be influenced by the presentation of answer choices, leaving the selec... | [
"Xue, Mengge",
"Hu, Zhenyu",
"Liu, Liqun",
"Liao, Kuo",
"Li, Shuang",
"Han, Honglin",
"Zhao, Meng",
"Yin, Chengguo"
] | Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors | acl-long.237 | Poster | 2406.01026v2 |
https://aclanthology.org/2024.acl-long.238.bib | @inproceedings{chen-etal-2024-sac,
title = "{SAC}-{KG}: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graph",
author = "Chen, Hanzhu and
Shen, Xu and
Lv, Qitan and
Wang, Jie and
Ni, Xiaoqi and
Ye, Jieping",
editor = "Ku, Lun-Wei a... | Knowledge graphs (KGs) play a pivotal role in knowledge-intensive tasks across specialized domains, where the acquisition of precise and dependable knowledge is crucial. However, existing KG construction methods heavily rely on human intervention to attain qualified KGs, which severely hinders the practical applicabili... | [
"Chen, Hanzhu",
"Shen, Xu",
"Lv, Qitan",
"Wang, Jie",
"Ni, Xiaoqi",
"Ye, Jieping"
] | {SAC}-{KG}: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graph | acl-long.238 | Poster | 2102.08827v2 |
https://aclanthology.org/2024.acl-long.239.bib | @inproceedings{yang-etal-2024-uncertainty-guided,
title = "Uncertainty-Guided Modal Rebalance for Hateful Memes Detection",
author = "Yang, Chuanpeng and
Liu, Yaxin and
Zhu, Fuqing and
Han, Jizhong and
Hu, Songlin",
editor = "Ku, Lun-Wei and
Martins, Andre and
Sriku... | Hateful memes detection is a challenging multimodal understanding task that requires comprehensive learning of vision, language, and cross-modal interactions. Previous research has focused on developing effective fusion strategies for integrating hate information from different modalities. However, these methods excess... | [
"Yang, Chuanpeng",
"Liu, Yaxin",
"Zhu, Fuqing",
"Han, Jizhong",
"Hu, Songlin"
] | Uncertainty-Guided Modal Rebalance for Hateful Memes Detection | acl-long.239 | Poster | 2212.06573v2 |
https://aclanthology.org/2024.acl-long.240.bib | @inproceedings{glockner-etal-2024-missci,
title = "Missci: Reconstructing Fallacies in Misrepresented Science",
author = "Glockner, Max and
Hou, Yufang and
Nakov, Preslav and
Gurevych, Iryna",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "... | Health-related misinformation on social networks can lead to poor decision-making and real-world dangers. Such misinformation often misrepresents scientific publications and cites them as {``}proof{''} to gain perceived credibility. To effectively counter such claims automatically, a system must explain how the claim w... | [
"Glockner, Max",
"Hou, Yufang",
"Nakov, Preslav",
"Gurevych, Iryna"
] | Missci: Reconstructing Fallacies in Misrepresented Science | acl-long.240 | Poster | 2406.03181v1 |
https://aclanthology.org/2024.acl-long.241.bib | @inproceedings{reich-schultz-2024-uncovering,
title = "Uncovering the Full Potential of Visual Grounding Methods in {VQA}",
author = "Reich, Daniel and
Schultz, Tanja",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting... | Visual Grounding (VG) methods in Visual Question Answering (VQA) attempt to improve VQA performance by strengthening a model{'}s reliance on question-relevant visual information. The presence of such relevant information in the visual input is typically assumed in training and testing. This assumption, however, is inhe... | [
"Reich, Daniel",
"Schultz, Tanja"
] | Uncovering the Full Potential of Visual Grounding Methods in {VQA} | acl-long.241 | Poster | 2401.07803v2 |
https://aclanthology.org/2024.acl-long.242.bib | @inproceedings{tan-etal-2024-small,
title = "Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for {LLM}s",
author = "Tan, Jiejun and
Dou, Zhicheng and
Zhu, Yutao and
Guo, Peidong and
Fang, Kun and
Wen, Ji-Rong",
editor = "Ku, Lun... | The integration of large language models (LLMs) and search engines represents a significant evolution in knowledge acquisition methodologies. However, determining the knowledge that an LLM already possesses and the knowledge that requires the help of a search engine remains an unresolved issue. Most existing methods so... | [
"Tan, Jiejun",
"Dou, Zhicheng",
"Zhu, Yutao",
"Guo, Peidong",
"Fang, Kun",
"Wen, Ji-Rong"
] | Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for {LLM}s | acl-long.242 | Poster | 2402.12052v3 |
https://aclanthology.org/2024.acl-long.243.bib | @inproceedings{von-daniken-etal-2024-favi,
title = "Favi-Score: A Measure for Favoritism in Automated Preference Ratings for Generative {AI} Evaluation",
author = {Von D{\"a}niken, Pius and
Deriu, Jan and
Tuggener, Don and
Cieliebak, Mark},
editor = "Ku, Lun-Wei and
Martins, Andr... | Generative AI systems have become ubiquitous for all kinds of modalities, which makes the issue of the evaluation of such models more pressing. One popular approach is preference ratings, where the generated outputs of different systems are shown to evaluators who choose their preferences. In recent years the field shi... | [
"Von D{\\\"a}niken, Pius",
"Deriu, Jan",
"Tuggener, Don",
"Cieliebak, Mark"
] | Favi-Score: A Measure for Favoritism in Automated Preference Ratings for Generative {AI} Evaluation | acl-long.243 | Poster | 2406.01131v1 |
https://aclanthology.org/2024.acl-long.244.bib | @inproceedings{ziegenbein-etal-2024-llm,
title = "{LLM}-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback",
author = "Ziegenbein, Timon and
Skitalinskaya, Gabriella and
Bayat Makou, Alireza and
Wachsmuth, Henning",
editor = "Ku, Lun-Wei a... | Ensuring that online discussions are civil and productive is a major challenge for social media platforms. Such platforms usually rely both on users and on automated detection tools to flag inappropriate arguments of other users, which moderators then review. However, this kind of post-hoc moderation is expensive and t... | [
"Ziegenbein, Timon",
"Skitalinskaya, Gabriella",
"Bayat Makou, Alireza",
"Wachsmuth, Henning"
] | {LLM}-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback | acl-long.244 | Poster | 2406.03363v1 |
https://aclanthology.org/2024.acl-long.245.bib | @inproceedings{plenz-frank-2024-graph,
title = "Graph Language Models",
author = "Plenz, Moritz and
Frank, Anette",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Vo... | While Language Models (LMs) are the workhorses of NLP, their interplay with structured knowledge graphs (KGs) is still actively researched. Current methods for encoding such graphs typically either (i) linearize them for embedding with LMs {--} which underutilize structural information, or (ii) use Graph Neural Network... | [
"Plenz, Moritz",
"Frank, Anette"
] | Graph Language Models | acl-long.245 | Oral | 2310.08487v1 |
https://aclanthology.org/2024.acl-long.246.bib | @inproceedings{periti-etal-2024-analyzing,
title = "Analyzing Semantic Change through Lexical Replacements",
author = "Periti, Francesco and
Cassotti, Pierluigi and
Dubossarsky, Haim and
Tahmasebi, Nina",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
b... | Modern language models are capable of contextualizing words based on their surrounding context. However, this capability is often compromised due to semantic change that leads to words being used in new, unexpected contexts not encountered during pre-training. In this paper, we model semantic change by studying the eff... | [
"Periti, Francesco",
"Cassotti, Pierluigi",
"Dubossarsky, Haim",
"Tahmasebi, Nina"
] | Analyzing Semantic Change through Lexical Replacements | acl-long.246 | Poster | 2404.18570v1 |
https://aclanthology.org/2024.acl-long.247.bib | @inproceedings{xu-etal-2024-exploiting,
title = "Exploiting Intrinsic Multilateral Logical Rules for Weakly Supervised Natural Language Video Localization",
author = "Xu, Zhe and
Wei, Kun and
Yang, Xu and
Deng, Cheng",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar,... | Weakly supervised natural language video localization (WS-NLVL) aims to retrieve the moment corresponding to a language query in a video with only video-language pairs utilized during training. Despite great success, existing WS-NLVL methods seldomly consider the complex temporal relations enclosing the language query ... | [
"Xu, Zhe",
"Wei, Kun",
"Yang, Xu",
"Deng, Cheng"
] | Exploiting Intrinsic Multilateral Logical Rules for Weakly Supervised Natural Language Video Localization | acl-long.247 | Poster | 1909.13784v2 |
https://aclanthology.org/2024.acl-long.248.bib | @inproceedings{weber-etal-2024-interpretability,
title = "Interpretability of Language Models via Task Spaces",
author = "Weber, Lucas and
Jumelet, Jaap and
Bruni, Elia and
Hupkes, Dieuwke",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Pr... | The usual way to interpret language models (LMs) is to test their performance on different benchmarks and subsequently infer their internal processes.In this paper, we present an alternative approach, concentrating on the {\_}quality{\_} of LM processing, with a focus on their language abilities.To this end, we constru... | [
"Weber, Lucas",
"Jumelet, Jaap",
"Bruni, Elia",
"Hupkes, Dieuwke"
] | Interpretability of Language Models via Task Spaces | acl-long.248 | Oral | 2406.06441v1 |
https://aclanthology.org/2024.acl-long.249.bib | @inproceedings{cassotti-etal-2024-using,
title = "Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Types",
author = "Cassotti, Pierluigi and
De Pascale, Stefano and
Tahmasebi, Nina",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
... | There is abundant evidence of the fact that the way words change their meaning can be classified in different types of change, highlighting the relationship between the old and new meanings (among which generalisation, specialisation and co-hyponymy transfer).In this paper, we present a way of detecting these types of ... | [
"Cassotti, Pierluigi",
"De Pascale, Stefano",
"Tahmasebi, Nina"
] | Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Types | acl-long.249 | Poster | 2406.03452v3 |
https://aclanthology.org/2024.acl-long.250.bib | @inproceedings{mahaut-etal-2024-factual,
title = "Factual Confidence of {LLM}s: on Reliability and Robustness of Current Estimators",
author = {Mahaut, Mat{\'e}o and
Aina, Laura and
Czarnowska, Paula and
Hardalov, Momchil and
M{\"u}ller, Thomas and
Marquez, Lluis},
editor ... | Large Language Models (LLMs) tend to be unreliable on fact-based answers.To address this problem, NLP researchers have proposed a range of techniques to estimate LLM{'}s confidence over facts. However, due to the lack of a systematic comparison, it is not clear how the different methods compare to one other.To fill thi... | [
"Mahaut, Mat{\\'e}o",
"Aina, Laura",
"Czarnowska, Paula",
"Hardalov, Momchil",
"M{\\\"u}ller, Thomas",
"Marquez, Lluis"
] | Factual Confidence of {LLM}s: on Reliability and Robustness of Current Estimators | acl-long.250 | Poster | 2406.13415v1 |
https://aclanthology.org/2024.acl-long.251.bib | @inproceedings{dou-etal-2024-stepcoder,
title = "{S}tep{C}oder: Improving Code Generation with Reinforcement Learning from Compiler Feedback",
author = "Dou, Shihan and
Liu, Yan and
Jia, Haoxiang and
Zhou, Enyu and
Xiong, Limao and
Shan, Junjie and
Huang, Caishuang and... | The advancement of large language models (LLMs) has significantly propelled the field of code generation. Previous work integrated reinforcement learning (RL) with compiler feedback for exploring the output space of LLMs to enhance code generation quality. However, the lengthy code generated by LLMs in response to comp... | [
"Dou, Shihan",
"Liu, Yan",
"Jia, Haoxiang",
"Zhou, Enyu",
"Xiong, Limao",
"Shan, Junjie",
"Huang, Caishuang",
"Wang, Xiao",
"Fan, Xiaoran",
"Xi, Zhiheng",
"Zhou, Yuhao",
"Ji, Tao",
"Zheng, Rui",
"Zhang, Qi",
"Gui, Tao",
"Huang, Xuanjing"
] | {S}tep{C}oder: Improving Code Generation with Reinforcement Learning from Compiler Feedback | acl-long.251 | Poster | 2203.05132v1 |
https://aclanthology.org/2024.acl-long.252.bib | @inproceedings{li-etal-2024-one,
title = "One-Shot Learning as Instruction Data Prospector for Large Language Models",
author = "Li, Yunshui and
Hui, Binyuan and
Xia, Xiaobo and
Yang, Jiaxi and
Yang, Min and
Zhang, Lei and
Si, Shuzheng and
Chen, Ling-Hao and
... | Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may compromise model performance. To address this challenge, we introduce Nuggets, a novel and efficient methodology that leverages one-shot learnin... | [
"Li, Yunshui",
"Hui, Binyuan",
"Xia, Xiaobo",
"Yang, Jiaxi",
"Yang, Min",
"Zhang, Lei",
"Si, Shuzheng",
"Chen, Ling-Hao",
"Liu, Junhao",
"Liu, Tongliang",
"Huang, Fei",
"Li, Yongbin"
] | One-Shot Learning as Instruction Data Prospector for Large Language Models | acl-long.252 | Poster | 2312.10302v4 |
https://aclanthology.org/2024.acl-long.253.bib | @inproceedings{shi-etal-2024-navigating,
title = "Navigating the {O}ver{K}ill in Large Language Models",
author = "Shi, Chenyu and
Wang, Xiao and
Ge, Qiming and
Gao, Songyang and
Yang, Xianjun and
Gui, Tao and
Zhang, Qi and
Huang, Xuanjing and
Zhao, Xun a... | Large language models are meticulously aligned to be both helpful and harmless. However, recent research points to a potential overkill which means models may refuse to answer benign queries. In this paper, we investigate the factors for overkill by exploring how models handle and determine the safety of queries. Our f... | [
"Shi, Chenyu",
"Wang, Xiao",
"Ge, Qiming",
"Gao, Songyang",
"Yang, Xianjun",
"Gui, Tao",
"Zhang, Qi",
"Huang, Xuanjing",
"Zhao, Xun",
"Lin, Dahua"
] | Navigating the {O}ver{K}ill in Large Language Models | acl-long.253 | Poster | 2401.17633v1 |
https://aclanthology.org/2024.acl-long.254.bib | @inproceedings{jacovi-etal-2024-chain,
title = "A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains",
author = "Jacovi, Alon and
Bitton, Yonatan and
Bohnet, Bernd and
Herzig, Jonathan and
Honovich, Or and
Tseng, Michael and
... | Prompting language models to provide step-by-step answers (e.g., {``}Chain-of-Thought{''}) is the prominent approach for complex reasoning tasks, where more accurate reasoning chains typically improve downstream task performance. Recent literature discusses automatic methods to verify reasoning to evaluate and improve ... | [
"Jacovi, Alon",
"Bitton, Yonatan",
"Bohnet, Bernd",
"Herzig, Jonathan",
"Honovich, Or",
"Tseng, Michael",
"Collins, Michael",
"Aharoni, Roee",
"Geva, Mor"
] | A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains | acl-long.254 | Oral | 2402.00559v4 |
https://aclanthology.org/2024.acl-long.255.bib | @inproceedings{ruan-etal-2024-re3,
title = "Re3: A Holistic Framework and Dataset for Modeling Collaborative Document Revision",
author = "Ruan, Qian and
Kuznetsov, Ilia and
Gurevych, Iryna",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedi... | Collaborative review and revision of textual documents is the core of knowledge work and a promising target for empirical analysis and NLP assistance. Yet, a holistic framework that would allow modeling complex relationships between document revisions, reviews and author responses is lacking. To address this gap, we in... | [
"Ruan, Qian",
"Kuznetsov, Ilia",
"Gurevych, Iryna"
] | Re3: A Holistic Framework and Dataset for Modeling Collaborative Document Revision | acl-long.255 | Poster | 2406.00197v1 |
https://aclanthology.org/2024.acl-long.256.bib | @inproceedings{czinczoll-etal-2024-nextlevelbert,
title = "{N}ext{L}evel{BERT}: Masked Language Modeling with Higher-Level Representations for Long Documents",
author = {Czinczoll, Tamara and
H{\"o}nes, Christoph and
Schall, Maximilian and
De Melo, Gerard},
editor = "Ku, Lun-Wei and
... | While (large) language models have significantly improved over the last years, they still struggle to sensibly process long sequences found, e.g., in books, due to the quadratic scaling of the underlying attention mechanism. To address this, we propose NextLevelBERT, a Masked Language Model operating not on tokens, but... | [
"Czinczoll, Tamara",
"H{\\\"o}nes, Christoph",
"Schall, Maximilian",
"De Melo, Gerard"
] | {N}ext{L}evel{BERT}: Masked Language Modeling with Higher-Level Representations for Long Documents | acl-long.256 | Poster | 2106.01040v3 |
https://aclanthology.org/2024.acl-long.257.bib | @inproceedings{jiang-etal-2024-followbench,
title = "{F}ollow{B}ench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models",
author = "Jiang, Yuxin and
Wang, Yufei and
Zeng, Xingshan and
Zhong, Wanjun and
Li, Liangyou and
Mi, Fei and
Shan... | The ability to follow instructions is crucial for Large Language Models (LLMs) to handle various real-world applications. Existing benchmarks primarily focus on evaluating pure response quality, rather than assessing whether the response follows constraints stated in the instruction. To fill this research gap, in this ... | [
"Jiang, Yuxin",
"Wang, Yufei",
"Zeng, Xingshan",
"Zhong, Wanjun",
"Li, Liangyou",
"Mi, Fei",
"Shang, Lifeng",
"Jiang, Xin",
"Liu, Qun",
"Wang, Wei"
] | {F}ollow{B}ench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models | acl-long.257 | Poster | 2406.13892v1 |
https://aclanthology.org/2024.acl-long.258.bib | @inproceedings{jiang-etal-2024-learning,
title = "Learning to Edit: Aligning {LLM}s with Knowledge Editing",
author = "Jiang, Yuxin and
Wang, Yufei and
Wu, Chuhan and
Zhong, Wanjun and
Zeng, Xingshan and
Gao, Jiahui and
Li, Liangyou and
Jiang, Xin and
Shan... | Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention. However, existing methods predominantly rely on memorizing the updated knowledge, impeding LLMs fro... | [
"Jiang, Yuxin",
"Wang, Yufei",
"Wu, Chuhan",
"Zhong, Wanjun",
"Zeng, Xingshan",
"Gao, Jiahui",
"Li, Liangyou",
"Jiang, Xin",
"Shang, Lifeng",
"Tang, Ruiming",
"Liu, Qun",
"Wang, Wei"
] | Learning to Edit: Aligning {LLM}s with Knowledge Editing | acl-long.258 | Poster | 2308.09954v1 |
https://aclanthology.org/2024.acl-long.259.bib | @inproceedings{wang-etal-2024-dolphcoder,
title = "{D}olph{C}oder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning",
author = "Wang, Yejie and
He, Keqing and
Dong, Guanting and
Wang, Pei and
Zeng, Weihao and
Diao, Muxi and
Xu... | Code Large Language Models (Code LLMs) have demonstrated outstanding performance in code-related tasks. Various instruction finetuning approaches have been proposed to boost the code generation performance of pre-trained Code LLMs. In this paper, we introduce a diverse instruction model DolphCoder with self-evaluating ... | [
"Wang, Yejie",
"He, Keqing",
"Dong, Guanting",
"Wang, Pei",
"Zeng, Weihao",
"Diao, Muxi",
"Xu, Weiran",
"Wang, Jingang",
"Zhang, Mengdi",
"Cai, Xunliang"
] | {D}olph{C}oder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning | acl-long.259 | Poster | 2403.00338v1 |
https://aclanthology.org/2024.acl-long.260.bib | @inproceedings{madureira-etal-2024-time,
title = "When Only Time Will Tell: Interpreting How Transformers Process Local Ambiguities Through the Lens of Restart-Incrementality",
author = "Madureira, Brielen and
Kahardipraja, Patrick and
Schlangen, David",
editor = "Ku, Lun-Wei and
Martin... | Incremental models that process sentences one token at a time will sometimes encounter points where more than one interpretation is possible. Causal models are forced to output one interpretation and continue, whereas models that can revise may edit their previous output as the ambiguity is resolved. In this work, we l... | [
"Madureira, Brielen",
"Kahardipraja, Patrick",
"Schlangen, David"
] | When Only Time Will Tell: Interpreting How Transformers Process Local Ambiguities Through the Lens of Restart-Incrementality | acl-long.260 | Poster | 2402.13113v2 |
https://aclanthology.org/2024.acl-long.261.bib | @inproceedings{rizvi-etal-2024-sparc,
title = "{S}pa{RC} and {S}pa{RP}: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models",
author = "Rizvi, Md Imbesat and
Zhu, Xiaodan and
Gurevych, Iryna",
editor = "Ku, Lun-Wei and... | Spatial reasoning is a crucial component of both biological and artificial intelligence. In this work, we present a comprehensive study of the capability of current state-of-the-art large language models (LLMs) on spatial reasoning. To support our study, we created and contribute a novel Spatial Reasoning Characterizat... | [
"Rizvi, Md Imbesat",
"Zhu, Xiaodan",
"Gurevych, Iryna"
] | {S}pa{RC} and {S}pa{RP}: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models | acl-long.261 | Poster | 2406.04566v1 |
https://aclanthology.org/2024.acl-long.262.bib | @inproceedings{he-etal-2024-planning,
title = "Planning Like Human: A Dual-process Framework for Dialogue Planning",
author = "He, Tao and
Liao, Lizi and
Cao, Yixin and
Liu, Yuanxing and
Liu, Ming and
Chen, Zerui and
Qin, Bing",
editor = "Ku, Lun-Wei and
Mart... | In proactive dialogue, the challenge lies not just in generating responses but in steering conversations toward predetermined goals, a task where Large Language Models (LLMs) typically struggle due to their reactive nature. Traditional approaches to enhance dialogue planning in LLMs, ranging from elaborate prompt engin... | [
"He, Tao",
"Liao, Lizi",
"Cao, Yixin",
"Liu, Yuanxing",
"Liu, Ming",
"Chen, Zerui",
"Qin, Bing"
] | Planning Like Human: A Dual-process Framework for Dialogue Planning | acl-long.262 | Poster | 2406.05374v1 |
https://aclanthology.org/2024.acl-long.263.bib | @inproceedings{cancedda-2024-spectral,
title = "Spectral Filters, Dark Signals, and Attention Sinks",
author = "Cancedda, Nicola",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguis... | Projecting intermediate representations onto the vocabulary is an increasingly popular interpretation tool for transformer-based LLMs, also known as the logit lens (Nostalgebraist). We propose a quantitative extension to this approach and define spectral filters on intermediate representations based on partitioning the... | [
"Cancedda, Nicola"
] | Spectral Filters, Dark Signals, and Attention Sinks | acl-long.263 | Poster | 2402.09221v1 |
https://aclanthology.org/2024.acl-long.264.bib | @inproceedings{gao-etal-2024-diffucomet,
title = "{D}iffu{COMET}: Contextual Commonsense Knowledge Diffusion",
author = "Gao, Silin and
Ismayilzada, Mete and
Zhao, Mengjie and
Wakaki, Hiromi and
Mitsufuji, Yuki and
Bosselut, Antoine",
editor = "Ku, Lun-Wei and
Marti... | Inferring contextually-relevant and diverse commonsense to understand narratives remains challenging for knowledge models. In this work, we develop a series of knowledge models, DiffuCOMET, that leverage diffusion to learn to reconstruct the implicit semantic connections between narrative contexts and relevant commonse... | [
"Gao, Silin",
"Ismayilzada, Mete",
"Zhao, Mengjie",
"Wakaki, Hiromi",
"Mitsufuji, Yuki",
"Bosselut, Antoine"
] | {D}iffu{COMET}: Contextual Commonsense Knowledge Diffusion | acl-long.264 | Poster | 2402.17011v1 |
https://aclanthology.org/2024.acl-long.265.bib | @inproceedings{sahinuc-etal-2024-systematic,
title = "Systematic Task Exploration with {LLM}s: A Study in Citation Text Generation",
author = "{\c{S}}ahinu{\c{c}}, Furkan and
Kuznetsov, Ilia and
Hou, Yufang and
Gurevych, Iryna",
editor = "Ku, Lun-Wei and
Martins, Andre and
... | Large language models (LLMs) bring unprecedented flexibility in defining and executing complex, creative natural language generation (NLG) tasks. Yet, this flexibility brings new challenges, as it introduces new degrees of freedom in formulating the task inputs and instructions and in evaluating model performance. To f... | [
"{\\c{S}}ahinu{\\c{c}}, Furkan",
"Kuznetsov, Ilia",
"Hou, Yufang",
"Gurevych, Iryna"
] | Systematic Task Exploration with {LLM}s: A Study in Citation Text Generation | acl-long.265 | Poster | 2407.04046v1 |
https://aclanthology.org/2024.acl-long.266.bib | @inproceedings{bortoletto-etal-2024-limits,
title = "Limits of Theory of Mind Modelling in Dialogue-Based Collaborative Plan Acquisition",
author = "Bortoletto, Matteo and
Ruhdorfer, Constantin and
Abdessaied, Adnen and
Shi, Lei and
Bulling, Andreas",
editor = "Ku, Lun-Wei and
... | Recent work on dialogue-based collaborative plan acquisition (CPA) has suggested that Theory of Mind (ToM) modelling can improve missing knowledge prediction in settings with asymmetric skill-sets and knowledge. Although ToM was claimed to be important for effective collaboration, its real impact on this novel task rem... | [
"Bortoletto, Matteo",
"Ruhdorfer, Constantin",
"Abdessaied, Adnen",
"Shi, Lei",
"Bulling, Andreas"
] | Limits of Theory of Mind Modelling in Dialogue-Based Collaborative Plan Acquisition | acl-long.266 | Poster | 2405.12621v2 |
https://aclanthology.org/2024.acl-long.267.bib | @inproceedings{chen-etal-2024-temporal,
title = "Temporal Knowledge Question Answering via Abstract Reasoning Induction",
author = "Chen, Ziyang and
Li, Dongfang and
Zhao, Xiang and
Hu, Baotian and
Zhang, Min",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, ... | In this study, we address the challenge of enhancing temporal knowledge reasoning in Large Language Models (LLMs). LLMs often struggle with this task, leading to the generation of inaccurate or misleading responses. This issue mainly arises from their limited ability to handle evolving factual knowledge and complex tem... | [
"Chen, Ziyang",
"Li, Dongfang",
"Zhao, Xiang",
"Hu, Baotian",
"Zhang, Min"
] | Temporal Knowledge Question Answering via Abstract Reasoning Induction | acl-long.267 | Poster | 2311.09149v2 |
https://aclanthology.org/2024.acl-long.268.bib | @inproceedings{lee-etal-2024-wrote,
title = "Who Wrote this Code? Watermarking for Code Generation",
author = "Lee, Taehyun and
Hong, Seokhee and
Ahn, Jaewoo and
Hong, Ilgee and
Lee, Hwaran and
Yun, Sangdoo and
Shin, Jamin and
Kim, Gunhee",
editor = "Ku, Lun-... | Since the remarkable generation performance of large language models raised ethical and legal concerns, approaches to detect machine-generated text by embedding watermarks are being developed.However, we discover that the existing works fail to function appropriately in code generation tasks due to the task{'}s nature ... | [
"Lee, Taehyun",
"Hong, Seokhee",
"Ahn, Jaewoo",
"Hong, Ilgee",
"Lee, Hwaran",
"Yun, Sangdoo",
"Shin, Jamin",
"Kim, Gunhee"
] | Who Wrote this Code? Watermarking for Code Generation | acl-long.268 | Poster | 2211.11883v1 |
https://aclanthology.org/2024.acl-long.269.bib | @inproceedings{islam-etal-2024-mapcoder,
title = "{M}ap{C}oder: Multi-Agent Code Generation for Competitive Problem Solving",
author = "Islam, Md. Ashraful and
Ali, Mohammed Eunus and
Parvez, Md Rizwan",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle... | Code synthesis, which requires a deep understanding of complex natural language (NL) problem descriptions, generation of code instructions for complex algorithms and data structures, and the successful execution of comprehensive unit tests, presents a significant challenge. Thus, while large language models (LLMs) demo... | [
"Islam, Md. Ashraful",
"Ali, Mohammed Eunus",
"Parvez, Md Rizwan"
] | {M}ap{C}oder: Multi-Agent Code Generation for Competitive Problem Solving | acl-long.269 | Poster | 2305.10679v1 |
https://aclanthology.org/2024.acl-long.270.bib | @inproceedings{zhu-etal-2024-relayattention,
title = "{R}elay{A}ttention for Efficient Large Language Model Serving with Long System Prompts",
author = "Zhu, Lei and
Wang, Xinjiang and
Zhang, Wayne and
Lau, Rynson",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vi... | A practical large language model (LLM) service may involve a long system prompt, which specifies the instructions, examples, and knowledge documents of the task and is reused across requests. However, the long system prompt causes throughput/latency bottlenecks as the cost of generating the next token grows w.r.t the s... | [
"Zhu, Lei",
"Wang, Xinjiang",
"Zhang, Wayne",
"Lau, Rynson"
] | {R}elay{A}ttention for Efficient Large Language Model Serving with Long System Prompts | acl-long.270 | Poster | 2402.14808v3 |
https://aclanthology.org/2024.acl-long.271.bib | @inproceedings{wang-etal-2024-boosting-language,
title = "Boosting Language Models Reasoning with Chain-of-Knowledge Prompting",
author = "Wang, Jianing and
Sun, Qiushi and
Li, Xiang and
Gao, Ming",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktit... | Recently, Chain-of-Thought (CoT) prompting has delivered success on complex reasoning tasks, which aims at designing a simple prompt like {``}Let{'}s think step by step{''} or multiple in-context exemplars with well-designed rationales to elicit Large Language Models (LLMs) to generate intermediate reasoning steps. How... | [
"Wang, Jianing",
"Sun, Qiushi",
"Li, Xiang",
"Gao, Ming"
] | Boosting Language Models Reasoning with Chain-of-Knowledge Prompting | acl-long.271 | Poster | 2304.05970v1 |
https://aclanthology.org/2024.acl-long.272.bib | @inproceedings{guo-etal-2024-open,
title = "Open Grounded Planning: Challenges and Benchmark Construction",
author = "Guo, Shiguang and
Deng, Ziliang and
Lin, Hongyu and
Lu, Yaojie and
Han, Xianpei and
Sun, Le",
editor = "Ku, Lun-Wei and
Martins, Andre and
Sr... | The emergence of large language models (LLMs) has increasingly drawn attention to the use of LLMs for human-like planning. Existing work on LLM-based planning either focuses on leveraging the inherent language generation capabilities of LLMs to produce free-style plans, or employs reinforcement learning approaches to l... | [
"Guo, Shiguang",
"Deng, Ziliang",
"Lin, Hongyu",
"Lu, Yaojie",
"Han, Xianpei",
"Sun, Le"
] | Open Grounded Planning: Challenges and Benchmark Construction | acl-long.272 | Poster | 2406.02903v1 |
https://aclanthology.org/2024.acl-long.273.bib | @inproceedings{xu-etal-2024-llm,
title = "{LLM} Knows Body Language, Too: Translating Speech Voices into Human Gestures",
author = "Xu, Chenghao and
Lyu, Guangtao and
Yan, Jiexi and
Yang, Muli and
Deng, Cheng",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, ... | In response to the escalating demand for digital human representations, progress has been made in the generation of realistic human gestures from given speeches. Despite the remarkable achievements of recent research, the generation process frequently includes unintended, meaningless, or non-realistic gestures. To addr... | [
"Xu, Chenghao",
"Lyu, Guangtao",
"Yan, Jiexi",
"Yang, Muli",
"Deng, Cheng"
] | {LLM} Knows Body Language, Too: Translating Speech Voices into Human Gestures | acl-long.273 | Poster | 2405.13336v1 |
https://aclanthology.org/2024.acl-long.274.bib | @inproceedings{huang-etal-2024-queryagent,
title = "{Q}uery{A}gent: A Reliable and Efficient Reasoning Framework with Environmental Feedback based Self-Correction",
author = "Huang, Xiang and
Cheng, Sitao and
Huang, Shanshan and
Shen, Jiayu and
Xu, Yong and
Zhang, Chaoyun and... | Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success. However, we find existing methods fall short in terms of reliability and efficiency when hallucinations are encountered. In this paper, we address these challenges with a framework called QueryAgent, which solves a question ste... | [
"Huang, Xiang",
"Cheng, Sitao",
"Huang, Shanshan",
"Shen, Jiayu",
"Xu, Yong",
"Zhang, Chaoyun",
"Qu, Yuzhong"
] | {Q}uery{A}gent: A Reliable and Efficient Reasoning Framework with Environmental Feedback based Self-Correction | acl-long.274 | Oral | 2403.11886v2 |
https://aclanthology.org/2024.acl-long.275.bib | @inproceedings{sun-etal-2024-pita,
title = "{PITA}: Prompting Task Interaction for Argumentation Mining",
author = "Sun, Yang and
Wang, Muyi and
Bao, Jianzhu and
Liang, Bin and
Zhao, Xiaoyan and
Yang, Caihua and
Yang, Min and
Xu, Ruifeng",
editor = "Ku, Lun-W... | Argumentation mining (AM) aims to detect the arguments and their inherent relations from argumentative textual compositions. Generally, AM comprises three key challenging subtasks, including argument component type classification (ACTC), argumentative relation identification (ARI), and argumentative relation type class... | [
"Sun, Yang",
"Wang, Muyi",
"Bao, Jianzhu",
"Liang, Bin",
"Zhao, Xiaoyan",
"Yang, Caihua",
"Yang, Min",
"Xu, Ruifeng"
] | {PITA}: Prompting Task Interaction for Argumentation Mining | acl-long.275 | Poster | 2404.02529v1 |
https://aclanthology.org/2024.acl-long.276.bib | @inproceedings{duan-etal-2024-shifting,
title = "Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models",
author = "Duan, Jinhao and
Cheng, Hao and
Wang, Shiqi and
Zavalny, Alex and
Wang, Chenan and
Xu, Renjing an... | Large Language Models (LLMs) show promising results in language generation and instruction following but frequently {``}hallucinate{''}, making their outputs less reliable. Despite Uncertainty Quantification{'}s (UQ) potential solutions, implementing it accurately within LLMs is challenging. Our research introduces a s... | [
"Duan, Jinhao",
"Cheng, Hao",
"Wang, Shiqi",
"Zavalny, Alex",
"Wang, Chenan",
"Xu, Renjing",
"Kailkhura, Bhavya",
"Xu, Kaidi"
] | Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models | acl-long.276 | Poster | 2408.06816v1 |
https://aclanthology.org/2024.acl-long.277.bib | @inproceedings{geigle-etal-2024-babel,
title = "Babel-{I}mage{N}et: Massively Multilingual Evaluation of Vision-and-Language Representations",
author = "Geigle, Gregor and
Timofte, Radu and
Glava{\v{s}}, Goran",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
b... | Vision-and-language (VL) models with separate encoders for each modality (e.g., CLIP) have become the go-to models for zero-shot image classification and image-text retrieval. They are, however, mostly evaluated in English as multilingual benchmarks are limited in availability. We introduce Babel-ImageNet, a massively ... | [
"Geigle, Gregor",
"Timofte, Radu",
"Glava{\\v{s}}, Goran"
] | Babel-{I}mage{N}et: Massively Multilingual Evaluation of Vision-and-Language Representations | acl-long.277 | Poster | 2303.15697v1 |
https://aclanthology.org/2024.acl-long.278.bib | @inproceedings{li-etal-2024-estimating,
title = "Estimating Agreement by Chance for Sequence Annotation",
author = "Li, Diya and
Rose, Carolyn and
Yuan, Ao and
Zhou, Chunxiao",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of t... | In the field of natural language processing, correction of performance assessment for chance agreement plays a crucial role in evaluating the reliability of annotations. However, there is a notable dearth of research focusing on chance correction for assessing the reliability of sequence annotation tasks, despite their... | [
"Li, Diya",
"Rose, Carolyn",
"Yuan, Ao",
"Zhou, Chunxiao"
] | Estimating Agreement by Chance for Sequence Annotation | acl-long.278 | Poster | 2407.11371v1 |
https://aclanthology.org/2024.acl-long.279.bib | @inproceedings{lu-etal-2024-emergent,
title = "Are Emergent Abilities in Large Language Models just In-Context Learning?",
author = "Lu, Sheng and
Bigoulaeva, Irina and
Sachdeva, Rachneet and
Tayyar Madabushi, Harish and
Gurevych, Iryna",
editor = "Ku, Lun-Wei and
Martins,... | Large language models, comprising billions of parameters and pre-trained on extensive web-scale corpora, have been claimed to acquire certain capabilities without having been specifically trained on them. These capabilities, referred to as {``}emergent abilities,{''} have been a driving force in discussions regarding t... | [
"Lu, Sheng",
"Bigoulaeva, Irina",
"Sachdeva, Rachneet",
"Tayyar Madabushi, Harish",
"Gurevych, Iryna"
] | Are Emergent Abilities in Large Language Models just In-Context Learning? | acl-long.279 | Poster | 2210.16433v3 |
https://aclanthology.org/2024.acl-long.280.bib | @inproceedings{yu-etal-2024-wavecoder,
title = "{W}ave{C}oder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning",
author = "Yu, Zhaojian and
Zhang, Xin and
Shang, Ning and
Huang, Yangyu and
Xu, Can and
Zhao, Yishujie and
Hu, Wenx... | Recent work demonstrates that, after instruction tuning, Code Large Language Models (Code LLMs) can obtain impressive capabilities to address a wide range of code-related tasks. However, current instruction tuning methods for Code LLMs mainly focus on the traditional code generation task, resulting in poor performance ... | [
"Yu, Zhaojian",
"Zhang, Xin",
"Shang, Ning",
"Huang, Yangyu",
"Xu, Can",
"Zhao, Yishujie",
"Hu, Wenxiang",
"Yin, Qiufeng"
] | {W}ave{C}oder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning | acl-long.280 | Poster | 2312.14187v5 |
https://aclanthology.org/2024.acl-long.281.bib | @inproceedings{li-etal-2024-eliciting-better,
title = "Eliciting Better Multilingual Structured Reasoning from {LLM}s through Code",
author = "Li, Bryan and
Alkhouli, Tamer and
Bonadiman, Daniele and
Pappas, Nikolaos and
Mansour, Saab",
editor = "Ku, Lun-Wei and
Martins, A... | The development of large language models (LLM) has shown progress on reasoning, though studies have largely considered either English or simple reasoning tasks. To address this, we introduce a multilingual structured reasoning and explanation dataset, termed xSTREET, that covers four tasks across six languages. xSTREET... | [
"Li, Bryan",
"Alkhouli, Tamer",
"Bonadiman, Daniele",
"Pappas, Nikolaos",
"Mansour, Saab"
] | Eliciting Better Multilingual Structured Reasoning from {LLM}s through Code | acl-long.281 | Poster | 2403.02567v2 |
https://aclanthology.org/2024.acl-long.282.bib | @inproceedings{ossowski-hu-2024-olive,
title = "{OLIVE}: Object Level In-Context Visual Embeddings",
author = "Ossowski, Timothy and
Hu, Junjie",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for... | Recent generalist vision-language models (VLMs) have demonstrated impressive reasoning capabilities across diverse multimodal tasks. However, these models still struggle with fine-grained object-level understanding and grounding. In terms of modeling, existing VLMs implicitly align text tokens with image patch tokens, ... | [
"Ossowski, Timothy",
"Hu, Junjie"
] | {OLIVE}: Object Level In-Context Visual Embeddings | acl-long.282 | Poster | 2009.09561v1 |
https://aclanthology.org/2024.acl-long.283.bib | @inproceedings{chen-mueller-2024-quantifying,
title = "Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness",
author = "Chen, Jiuhai and
Mueller, Jonas",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings... | We introduce BSDetector, a method for detecting bad and speculative answers from a pretrained Large Language Model by estimating a numeric confidence score for any output it generated. Our uncertainty quantification technique works for any LLM accessible only via a black-box API, whose training data remains unknown. By... | [
"Chen, Jiuhai",
"Mueller, Jonas"
] | Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness | acl-long.283 | Poster | 2308.16175v2 |
https://aclanthology.org/2024.acl-long.284.bib | @inproceedings{zhang-etal-2024-marathon,
title = "Marathon: A Race Through the Realm of Long Context with Large Language Models",
author = "Zhang, Lei and
Li, Yunshui and
Liu, Ziqiang and
Yang, Jiaxi and
Liu, Junhao and
Chen, Longze and
Luo, Run and
Yang, Min",
... | With the advancement of large language models (LLMs) and the expansion of their context windows, existing long-context benchmarks fall short in effectively evaluating the models{'} comprehension and reasoning abilities in extended texts. Moreover, conventional benchmarks relying on F1 metrics often inaccurately score r... | [
"Zhang, Lei",
"Li, Yunshui",
"Liu, Ziqiang",
"Yang, Jiaxi",
"Liu, Junhao",
"Chen, Longze",
"Luo, Run",
"Yang, Min"
] | Marathon: A Race Through the Realm of Long Context with Large Language Models | acl-long.284 | Poster | 2312.09542v2 |
https://aclanthology.org/2024.acl-long.285.bib | @inproceedings{gao-etal-2024-beyond,
title = "Beyond Scaling: Predicting Patent Approval with Domain-specific Fine-grained Claim Dependency Graph",
author = "Gao, Xiaochen and
Yao, Feng and
Zhao, Kewen and
He, Beilei and
Kumar, Animesh and
Krishnan, Vish and
Shang, Jing... | Model scaling is becoming the default choice for many language tasks due to the success of large language models (LLMs). However, it can fall short in specific scenarios where simple customized methods excel. In this paper, we delve into the patent approval prediction task and unveil that simple domain-specific graph m... | [
"Gao, Xiaochen",
"Yao, Feng",
"Zhao, Kewen",
"He, Beilei",
"Kumar, Animesh",
"Krishnan, Vish",
"Shang, Jingbo"
] | Beyond Scaling: Predicting Patent Approval with Domain-specific Fine-grained Claim Dependency Graph | acl-long.285 | Oral | 2404.14372v1 |
https://aclanthology.org/2024.acl-long.286.bib | @inproceedings{zhuang-etal-2024-pcad,
title = "{PCAD}: Towards {ASR}-Robust Spoken Language Understanding via Prototype Calibration and Asymmetric Decoupling",
author = "Zhuang, Xianwei and
Cheng, Xuxin and
Liang, Liming and
Xie, Yuxin and
Wang, Zhichang and
Huang, Zhiqi and
... | Spoken language understanding (SLU) inevitably suffers from error propagation from automatic speech recognition (ASR) in actual scenarios. Some recent works attempt to alleviate this issue through contrastive learning. However, they (1) sample negative pairs incorrectly in pre-training; (2) only focus on implicit metri... | [
"Zhuang, Xianwei",
"Cheng, Xuxin",
"Liang, Liming",
"Xie, Yuxin",
"Wang, Zhichang",
"Huang, Zhiqi",
"Zou, Yuexian"
] | {PCAD}: Towards {ASR}-Robust Spoken Language Understanding via Prototype Calibration and Asymmetric Decoupling | acl-long.286 | Poster | 9411028v1 |
https://aclanthology.org/2024.acl-long.287.bib | @inproceedings{jin-etal-2024-rethinking,
title = "Rethinking the Multimodal Correlation of Multimodal Sequential Learning via Generalizable Attentional Results Alignment",
author = "Jin, Tao and
Lin, Wang and
Wang, Ye and
Li, Linjun and
Cheng, Xize and
Zhao, Zhou",
editor ... | Transformer-based methods have gone mainstream in multimodal sequential learning. The intra and inter modality interactions are captured by the query-key associations of multi-head attention. In this way, the calculated multimodal contexts (attentional results) are expected to be relevant to the query modality. However... | [
"Jin, Tao",
"Lin, Wang",
"Wang, Ye",
"Li, Linjun",
"Cheng, Xize",
"Zhao, Zhou"
] | Rethinking the Multimodal Correlation of Multimodal Sequential Learning via Generalizable Attentional Results Alignment | acl-long.287 | Poster | 2407.03836v1 |
https://aclanthology.org/2024.acl-long.288.bib | @inproceedings{liang-etal-2024-uhgeval,
title = "{UHGE}val: Benchmarking the Hallucination of {C}hinese Large Language Models via Unconstrained Generation",
author = "Liang, Xun and
Song, Shichao and
Niu, Simin and
Li, Zhiyu and
Xiong, Feiyu and
Tang, Bo and
Wang, Yezha... | Large language models (LLMs) produce hallucinated text, compromising their practical utility in professional contexts. To assess the reliability of LLMs, numerous initiatives have developed benchmark evaluations for hallucination phenomena. However, they often employ constrained generation techniques to produce the eva... | [
"Liang, Xun",
"Song, Shichao",
"Niu, Simin",
"Li, Zhiyu",
"Xiong, Feiyu",
"Tang, Bo",
"Wang, Yezhaohui",
"He, Dawei",
"Peng, Cheng",
"Wang, Zhonghao",
"Deng, Haiying"
] | {UHGE}val: Benchmarking the Hallucination of {C}hinese Large Language Models via Unconstrained Generation | acl-long.288 | Poster | 2311.15296v3 |
https://aclanthology.org/2024.acl-long.289.bib | @inproceedings{lin-etal-2024-preflmr,
title = "{P}re{FLMR}: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers",
author = "Lin, Weizhe and
Mei, Jingbiao and
Chen, Jinghong and
Byrne, Bill",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
boo... | Large Multimodal Models (LMMs) excel in natural language and visual understanding but are challenged by exacting tasks such as Knowledge-based Visual Question Answering (KB-VQA) which involve the retrieval of relevant information from document collections to use in shaping answers to questions. We present an extensive ... | [
"Lin, Weizhe",
"Mei, Jingbiao",
"Chen, Jinghong",
"Byrne, Bill"
] | {P}re{FLMR}: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers | acl-long.289 | Poster | 1712.09550v2 |
https://aclanthology.org/2024.acl-long.290.bib | @inproceedings{erker-etal-2024-triple,
title = "Triple-Encoders: Representations That Fire Together, Wire Together",
author = "Erker, Justus-Jonas and
Mai, Florian and
Reimers, Nils and
Spanakis, Gerasimos and
Gurevych, Iryna",
editor = "Ku, Lun-Wei and
Martins, Andre and... | Search-based dialog models typically re-encode the dialog history at every turn, incurring high cost.Curved Contrastive Learning, a representation learning method that encodes relative distances between utterances into the embedding space via a bi-encoder, has recently shown promising results for dialog modeling at far... | [
"Erker, Justus-Jonas",
"Mai, Florian",
"Reimers, Nils",
"Spanakis, Gerasimos",
"Gurevych, Iryna"
] | Triple-Encoders: Representations That Fire Together, Wire Together | acl-long.290 | Poster | 2110.08232v4 |
https://aclanthology.org/2024.acl-long.291.bib | @inproceedings{mei-etal-2024-improving,
title = "Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning",
author = "Mei, Jingbiao and
Chen, Jinghong and
Lin, Weizhe and
Byrne, Bill and
Tomalin, Marcus",
editor = "Ku, Lun-Wei and
Martins, Andre and
... | Hateful memes have emerged as a significant concern on the Internet. Detecting hateful memes requires the system to jointly understand the visual and textual modalities. Our investigation reveals that the embedding space of existing CLIP-based systems lacks sensitivity to subtle differences in memes that are vital for ... | [
"Mei, Jingbiao",
"Chen, Jinghong",
"Lin, Weizhe",
"Byrne, Bill",
"Tomalin, Marcus"
] | Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning | acl-long.291 | Poster | 2311.08110v2 |
https://aclanthology.org/2024.acl-long.292.bib | @inproceedings{zhang-etal-2024-agent,
title = "Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization",
author = "Zhang, Wenqi and
Tang, Ke and
Wu, Hai and
Wang, Mengna and
Shen, Yongliang and
Hou, Guiyang and
Tan, Zeqi and
Li, Peng and
... | Large Language Models (LLMs) exhibit robust problem-solving capabilities for diverse tasks. However, most LLM-based agents are designed as specific task solvers with sophisticated prompt engineering, rather than agents capable of learning and evolving through interactions. These task solvers necessitate manually crafte... | [
"Zhang, Wenqi",
"Tang, Ke",
"Wu, Hai",
"Wang, Mengna",
"Shen, Yongliang",
"Hou, Guiyang",
"Tan, Zeqi",
"Li, Peng",
"Zhuang, Yueting",
"Lu, Weiming"
] | Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization | acl-long.292 | Poster | 2402.17574v3 |
https://aclanthology.org/2024.acl-long.293.bib | @inproceedings{razzhigaev-etal-2024-transformer,
title = "Your Transformer is Secretly Linear",
author = "Razzhigaev, Anton and
Mikhalchuk, Matvey and
Goncharova, Elizaveta and
Gerasimenko, Nikolai and
Oseledets, Ivan and
Dimitrov, Denis and
Kuznetsov, Andrey",
edit... | This paper reveals a novel linear characteristic exclusive to transformer decoders, including models like GPT, LLaMA, OPT, BLOOM and others. We analyze embedding transformations between sequential layers, uncovering an almost perfect linear relationship (Procrustes similarity score of 0.99). However, linearity decrease... | [
"Razzhigaev, Anton",
"Mikhalchuk, Matvey",
"Goncharova, Elizaveta",
"Gerasimenko, Nikolai",
"Oseledets, Ivan",
"Dimitrov, Denis",
"Kuznetsov, Andrey"
] | Your Transformer is Secretly Linear | acl-long.293 | Poster | 2405.12250v1 |
https://aclanthology.org/2024.acl-long.294.bib | @inproceedings{jinadu-ding-2024-noise,
title = "Noise Correction on Subjective Datasets",
author = "Jinadu, Uthman and
Ding, Yi",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational L... | Incorporating every annotator{'}s perspective is crucial for unbiased data modeling. Annotator fatigue and changing opinions over time can distort dataset annotations. To combat this, we propose to learn a more accurate representation of diverse opinions by utilizing multitask learning in conjunction with loss-based la... | [
"Jinadu, Uthman",
"Ding, Yi"
] | Noise Correction on Subjective Datasets | acl-long.294 | Poster | 2206.10609v1 |
https://aclanthology.org/2024.acl-long.295.bib | @inproceedings{senel-etal-2024-generative,
title = "Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using {LLM} Optimizers",
author = {Senel, L{\"u}tfi Kerem and
Fetahu, Besnik and
Yoshida, Davis and
Chen, Zhiyu and
Castellucci, Giuseppe and
... | Recommender systems are widely used to suggest engaging content, and Large Language Models (LLMs) have given rise to generative recommenders. Such systems can directly generate items, including for open-set tasks like question suggestion. While the world knowledge of LLMs enables good recommendations, improving the gen... | [
"Senel, L{\\\"u}tfi Kerem",
"Fetahu, Besnik",
"Yoshida, Davis",
"Chen, Zhiyu",
"Castellucci, Giuseppe",
"Vedula, Nikhita",
"Choi, Jason Ingyu",
"Malmasi, Shervin"
] | Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using {LLM} Optimizers | acl-long.295 | Poster | 2406.05255v1 |
https://aclanthology.org/2024.acl-long.296.bib | @inproceedings{jiang-etal-2024-instruction,
title = "Instruction-tuned Language Models are Better Knowledge Learners",
author = "Jiang, Zhengbao and
Sun, Zhiqing and
Shi, Weijia and
Rodriguez, Pedro and
Zhou, Chunting and
Neubig, Graham and
Lin, Xi and
Yih, Wen-t... | In order for large language model (LLM)-based assistants to effectively adapt to evolving information needs, it must be possible to update their factual knowledge through continued training on new data. The standard recipe for doing so involves continued pre-training on new documents followed by instruction-tuning on q... | [
"Jiang, Zhengbao",
"Sun, Zhiqing",
"Shi, Weijia",
"Rodriguez, Pedro",
"Zhou, Chunting",
"Neubig, Graham",
"Lin, Xi",
"Yih, Wen-tau",
"Iyer, Srini"
] | Instruction-tuned Language Models are Better Knowledge Learners | acl-long.296 | Poster | 2306.14101v1 |
https://aclanthology.org/2024.acl-long.297.bib | @inproceedings{ngo-kim-2024-language,
title = "What Do Language Models Hear? Probing for Auditory Representations in Language Models",
author = "Ngo, Jerry and
Kim, Yoon",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meet... | This work explores whether language models encode meaningfully grounded representations of sounds of objects. We learn a linear probe that retrieves the correct text representation of an object given a snippet of audio related to that object, where the sound representation is given by a pretrained audio model. This pro... | [
"Ngo, Jerry",
"Kim, Yoon"
] | What Do Language Models Hear? Probing for Auditory Representations in Language Models | acl-long.297 | Poster | 2402.16998v1 |
https://aclanthology.org/2024.acl-long.298.bib | @inproceedings{kim-etal-2024-threads,
title = "Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs",
author = "Kim, Zae Myung and
Lee, Kwang and
Zhu, Preston and
Raheja, Vipul and
Kang, Dongyeop",
editor = "Ku, Lun-Wei and
Martins, Andre and
... | With the advent of large language models (LLM), the line between human-crafted and machine-generated texts has become increasingly blurred. This paper delves into the inquiry of identifying discernible and unique linguistic properties in texts that were written by humans, particularly uncovering the underlying discours... | [
"Kim, Zae Myung",
"Lee, Kwang",
"Zhu, Preston",
"Raheja, Vipul",
"Kang, Dongyeop"
] | Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs | acl-long.298 | Poster | 2402.10586v2 |
https://aclanthology.org/2024.acl-long.299.bib | @inproceedings{zhang-etal-2024-jailbreak,
title = "Jailbreak Open-Sourced Large Language Models via Enforced Decoding",
author = "Zhang, Hangfan and
Guo, Zhimeng and
Zhu, Huaisheng and
Cao, Bochuan and
Lin, Lu and
Jia, Jinyuan and
Chen, Jinghui and
Wu, Dinghao",
... | Large Language Models (LLMs) have achieved unprecedented performance in Natural Language Generation (NLG) tasks. However, many existing studies have shown that they could be misused to generate undesired content. In response, before releasing LLMs for public access, model developers usually align those language models ... | [
"Zhang, Hangfan",
"Guo, Zhimeng",
"Zhu, Huaisheng",
"Cao, Bochuan",
"Lin, Lu",
"Jia, Jinyuan",
"Chen, Jinghui",
"Wu, Dinghao"
] | Jailbreak Open-Sourced Large Language Models via Enforced Decoding | acl-long.299 | Poster | 2406.09289v1 |
https://aclanthology.org/2024.acl-long.300.bib | @inproceedings{srivastava-etal-2024-nice,
title = "{NICE}: To Optimize In-Context Examples or Not?",
author = "Srivastava, Pragya and
Golechha, Satvik and
Deshpande, Amit and
Sharma, Amit",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Pro... | Recent work shows that in-context learning and optimization of in-context examples (ICE) can significantly improve the accuracy of large language models (LLMs) on a wide range of tasks, leading to an apparent consensus that ICE optimization is crucial for better performance. However, most of these studies assume a fixe... | [
"Srivastava, Pragya",
"Golechha, Satvik",
"Deshp",
"e, Amit",
"Sharma, Amit"
] | {NICE}: To Optimize In-Context Examples or Not? | acl-long.300 | Poster | 9709228v1 |
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