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YimingZeng/FineEdit_bench

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Citation text from the card

@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