| アイテムタイプ |
会議発表論文 / Conference Paper(1) |
| 公開日 |
2025-07-24 |
| タイトル |
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タイトル |
InstructCMP: Length Control in Sentence Compression through Instruction-based Large Language Models |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ |
conference paper |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Juseon-Do
Kwon, Jingun
上垣外, 英剛
Okumura, Manabu
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
Extractive summarization can produce faithful summaries but often requires additional constraints such as a desired summary length. Traditional sentence compression models do not typically consider the constraints because of their restricted model abilities, which require model modifications for coping with them. To bridge this gap, we propose Instruction-based Compression (InstructCMP), an approach to the sentence compression task that can consider the length constraint through instructions by leveraging the zero-shot task-solving abilities of Large Language Models (LLMs). For this purpose, we created new evaluation datasets by transforming traditional sentence compression datasets into an instruction format. By using the datasets, we first reveal that the current LLMs still face challenges in accurately controlling the length for a compressed text. To address this issue, we propose an approach named length priming, that incorporates additional length information into the instructions without external resources. While the length priming effectively works in a zero-shot setting, a training dataset with the instructions would further improve the ability of length control. Thus, we additionally created a training dataset in an instruction format to fine-tune the model on it. Experimental results and analysis show that applying the length priming significantly improves performances of InstructCMP in both zero-shot and fine-tuning settings without the need of any model modifications. |
| 書誌情報 |
en : Findings of the Association for Computational Linguistics: ACL 2024
p. 8980-8996,
ページ数 17,
発行日 2024-08
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| 会議情報 |
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会議名 |
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) |
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回次 |
62 |
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開始年 |
2024 |
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開始月 |
08 |
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開始日 |
11 |
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終了年 |
2024 |
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終了月 |
08 |
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終了日 |
16 |
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開催期間 |
2024-08-11 - 2024-08-16 |
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開催地 |
Bangkok, Thailand |
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開催国 |
THA |
| 出版者 |
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出版者 |
Association for Computational Linguistics |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.18653/v1/2024.findings-acl.532 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://aclanthology.org/2024.findings-acl.532/ |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
$00A92024 Association for Computational Linguistics. ACL materials are Copyright $00A9 1963$20132025 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License. |
| 著者版フラグ |
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出版タイプ |
NA |
