@inproceedings{zeng-etal-2025-bridging,
title = "Bridging the Editing Gap in {LLM}s: {F}ine{E}dit for Precise and Targeted Text Modifications",
author = "Zeng, Yiming and
Yu, Wanhao and
Li, Zexin and
Ren, Tao and
Ma, Yu and
Cao, Jinghan and
Chen, Xiyan and
Yu, Tingting",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.118/",
pages = "2193--2206",
ISBN = "979-8-89176-335-7",
abstract = "Large Language Models (LLMs) have significantly advanced natural language processing, demonstrating strong capabilities in tasks such as text generation, summarization, and reasoning. Recently, their potential for automating precise text editing tasks across specialized domains, such as programming code, LaTeX, and structured database languages, has gained attention. However, current state-of-the-art LLMs still struggle with executing precise, instruction-driven edits, particularly when structural accuracy and strict adherence to domain conventions are required.To address these challenges, we introduce InstrEditBench, an automated benchmark dataset comprising over 30,000 structured editing tasks spanning diverse domains, including Wikipedia articles, LaTeX documents, source code, and database languages. Using this benchmark, we develop FineEdit, a specialized editing model explicitly trained for accurate, context-aware text modifications. Experimental evaluations demonstrate that FineEdit outperforms state-of-the-art models, achieving improvements of approximately 10{\%} over Gemini models on single-turn edits, up to 30{\%} over Llama-3.2-3B, and exceeding Mistral-7B-OpenOrca performance by over 40{\%} on direct editing tasks. FineEdit also effectively generalizes to realistic multi-turn editing scenarios, highlighting its practical applicability. To facilitate further research and reproducibility, we release FineEdit at \url{https://github.com/StuRinDQB/FineEdit} and \url{https://huggingface.co/datasets/YimingZeng/FineEdit_bench}."
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<abstract>Large Language Models (LLMs) have significantly advanced natural language processing, demonstrating strong capabilities in tasks such as text generation, summarization, and reasoning. Recently, their potential for automating precise text editing tasks across specialized domains, such as programming code, LaTeX, and structured database languages, has gained attention. However, current state-of-the-art LLMs still struggle with executing precise, instruction-driven edits, particularly when structural accuracy and strict adherence to domain conventions are required.To address these challenges, we introduce InstrEditBench, an automated benchmark dataset comprising over 30,000 structured editing tasks spanning diverse domains, including Wikipedia articles, LaTeX documents, source code, and database languages. Using this benchmark, we develop FineEdit, a specialized editing model explicitly trained for accurate, context-aware text modifications. Experimental evaluations demonstrate that FineEdit outperforms state-of-the-art models, achieving improvements of approximately 10% over Gemini models on single-turn edits, up to 30% over Llama-3.2-3B, and exceeding Mistral-7B-OpenOrca performance by over 40% on direct editing tasks. FineEdit also effectively generalizes to realistic multi-turn editing scenarios, highlighting its practical applicability. To facilitate further research and reproducibility, we release FineEdit at https://github.com/StuRinDQB/FineEdit and https://huggingface.co/datasets/YimingZeng/FineEdit_bench.</abstract>
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%0 Conference Proceedings
%T Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications
%A Zeng, Yiming
%A Yu, Wanhao
%A Li, Zexin
%A Ren, Tao
%A Ma, Yu
%A Cao, Jinghan
%A Chen, Xiyan
%A Yu, Tingting
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F zeng-etal-2025-bridging
%X Large Language Models (LLMs) have significantly advanced natural language processing, demonstrating strong capabilities in tasks such as text generation, summarization, and reasoning. Recently, their potential for automating precise text editing tasks across specialized domains, such as programming code, LaTeX, and structured database languages, has gained attention. However, current state-of-the-art LLMs still struggle with executing precise, instruction-driven edits, particularly when structural accuracy and strict adherence to domain conventions are required.To address these challenges, we introduce InstrEditBench, an automated benchmark dataset comprising over 30,000 structured editing tasks spanning diverse domains, including Wikipedia articles, LaTeX documents, source code, and database languages. Using this benchmark, we develop FineEdit, a specialized editing model explicitly trained for accurate, context-aware text modifications. Experimental evaluations demonstrate that FineEdit outperforms state-of-the-art models, achieving improvements of approximately 10% over Gemini models on single-turn edits, up to 30% over Llama-3.2-3B, and exceeding Mistral-7B-OpenOrca performance by over 40% on direct editing tasks. FineEdit also effectively generalizes to realistic multi-turn editing scenarios, highlighting its practical applicability. To facilitate further research and reproducibility, we release FineEdit at https://github.com/StuRinDQB/FineEdit and https://huggingface.co/datasets/YimingZeng/FineEdit_bench.
%U https://aclanthology.org/2025.findings-emnlp.118/
%P 2193-2206
Markdown (Informal)
[Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications](https://aclanthology.org/2025.findings-emnlp.118/) (Zeng et al., Findings 2025)
ACL
- Yiming Zeng, Wanhao Yu, Zexin Li, Tao Ren, Yu Ma, Jinghan Cao, Xiyan Chen, and Tingting Yu. 2025. Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 2193–2206, Suzhou, China. Association for Computational Linguistics.