@inproceedings{liu-litman-2026-modeling,
title = "Modeling Iterative Text Revisions Through Chain of Revisions",
author = "Liu, Zhexiong and
Litman, Diane",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-short.23/",
pages = "295--307",
ISBN = "979-8-89176-440-8",
abstract = "Text revision has become an essential part of writing, as it often improves the text through iterative refinement. However, large language models (LLMs) have primarily regarded text revision as a holistic task that simultaneously revises grammar, logic, and content. This might be because LLMs are pre-trained only to generate final text rather than to learn the iterative revision process. Therefore, such LLMs largely overlook the dynamic nature of human revision, where writers progressively improve their writing through a sequence of intended edits. To bridge the gap, we investigate text revision generation from an iterativeness perspective and propose a chain of revisions that focuses on revision orders to iteratively improve text quality. Experimental results suggest that our proposed revision chains effectively capture the dynamics of text revision in multi-intent scenarios and outperform holistic revision baselines, which provide a valuable pilot study for iterative revision research."
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<abstract>Text revision has become an essential part of writing, as it often improves the text through iterative refinement. However, large language models (LLMs) have primarily regarded text revision as a holistic task that simultaneously revises grammar, logic, and content. This might be because LLMs are pre-trained only to generate final text rather than to learn the iterative revision process. Therefore, such LLMs largely overlook the dynamic nature of human revision, where writers progressively improve their writing through a sequence of intended edits. To bridge the gap, we investigate text revision generation from an iterativeness perspective and propose a chain of revisions that focuses on revision orders to iteratively improve text quality. Experimental results suggest that our proposed revision chains effectively capture the dynamics of text revision in multi-intent scenarios and outperform holistic revision baselines, which provide a valuable pilot study for iterative revision research.</abstract>
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%0 Conference Proceedings
%T Modeling Iterative Text Revisions Through Chain of Revisions
%A Liu, Zhexiong
%A Litman, Diane
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-440-8
%F liu-litman-2026-modeling
%X Text revision has become an essential part of writing, as it often improves the text through iterative refinement. However, large language models (LLMs) have primarily regarded text revision as a holistic task that simultaneously revises grammar, logic, and content. This might be because LLMs are pre-trained only to generate final text rather than to learn the iterative revision process. Therefore, such LLMs largely overlook the dynamic nature of human revision, where writers progressively improve their writing through a sequence of intended edits. To bridge the gap, we investigate text revision generation from an iterativeness perspective and propose a chain of revisions that focuses on revision orders to iteratively improve text quality. Experimental results suggest that our proposed revision chains effectively capture the dynamics of text revision in multi-intent scenarios and outperform holistic revision baselines, which provide a valuable pilot study for iterative revision research.
%U https://aclanthology.org/2026.aacl-short.23/
%P 295-307
Markdown (Informal)
[Modeling Iterative Text Revisions Through Chain of Revisions](https://aclanthology.org/2026.aacl-short.23/) (Liu & Litman, AACL-IJCNLP 2026)
ACL
- Zhexiong Liu and Diane Litman. 2026. Modeling Iterative Text Revisions Through Chain of Revisions. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 295–307, Hengqin, China. Association for Computational Linguistics.