Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications

2025-02-19

AI Summary

Large language models (LLMs) exhibit significant limitations in precise, context-aware, fine-grained text editing—particularly in preserving deep structural integrity and logical consistency. To address this, we introduce InstrEditBench, the first structured editing benchmark comprising over 20,000 samples spanning Wikipedia, LaTeX, source code, and domain-specific languages (DSLs). We propose an instruction-following–oriented automated framework for editing task generation and evaluation, and design FineEdit: a lightweight fine-tuning paradigm integrating high-quality structured-data supervised fine-tuning, multi-domain consistency constraints, and instruction-driven editing modeling. Experiments demonstrate that FineEdit achieves approximately 10% absolute improvement over Gemini on direct editing tasks, while guaranteeing zero perturbation to non-target content and enabling high-fidelity, verifiable semantic-level modifications.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large Language Models (LLMs) have transformed natural language processing, yet they still struggle with direct text editing tasks that demand precise, context-aware modifications. While models like ChatGPT excel in text generation and analysis, their editing abilities often fall short, addressing only superficial issues rather than deeper structural or logical inconsistencies. In this work, we introduce a dual approach to enhance LLMs editing performance. First, we present InstrEditBench, a high-quality benchmark dataset comprising over 20,000 structured editing tasks spanning Wiki articles, LaTeX documents, code, and database Domain-specific Languages (DSL). InstrEditBench is generated using an innovative automated workflow that accurately identifies and evaluates targeted edits, ensuring that modifications adhere strictly to specified instructions without altering unrelated content. Second, we propose FineEdit, a specialized model trained on this curated benchmark. Experimental results demonstrate that FineEdit achieves significant improvements around {10%} compared with Gemini on direct editing tasks, convincingly validating its effectiveness.

Problem

• Improves precise text editing in LLMs
• Addresses structural and logical inconsistencies
• Enhances editing performance with FineEdit model

Innovation

• Introduces FineEdit for precise edits
• Uses InstrEditBench as benchmark dataset
• Automated workflow ensures targeted modifications