All datasets / YimingZeng
YimingZeng/FineEdit_bench
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@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 e
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dataset_licence({ "id": "YimingZeng/FineEdit_bench" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0