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

arXiv: https://arxiv.org/abs/2502.13358 · HTML: https://mlsanity.com/paper/2502.13358/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 57 references that are in the map.
- First posted on arXiv 19 Feb 2025.

## Builds on (most cited first, 27 of 27)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://mlsanity.com/paper/2301.13688/) (2023)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [Editing Large Language Models: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2305.13172/) (2023)
- [Evaluation of Text Generation: A Survey](https://mlsanity.com/paper/2006.14799/) (2020)
- [Style Transfer from Non-Parallel Text by Cross-Alignment](https://mlsanity.com/paper/1705.09655/) (2017)
- [CoEdIT: Text Editing by Task-Specific Instruction Tuning](https://mlsanity.com/paper/2305.09857/) (2023)
- [Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models](https://mlsanity.com/paper/2105.13318/) (2021)
- [Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions](https://mlsanity.com/paper/2312.12450/) (2023)
- [Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework](https://mlsanity.com/paper/2210.16798/) (2022)
- [Exploring the Capabilities of LLMs for Code Change Related Tasks](https://mlsanity.com/paper/2407.02824/) (2024)
- [Prompt-Based Editing for Text Style Transfer](https://mlsanity.com/paper/2301.11997/) (2023)
- [Unprecedented Code Change Automation: The Fusion of LLMs and Transformation by Example](https://mlsanity.com/paper/2402.07138/) (2024)
- [Dynamic Transformers Provide a False Sense of Efficiency](https://mlsanity.com/paper/2305.12228/) (2023)
- [Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-Review](https://mlsanity.com/paper/2410.03663/) (2024)
- [Teaching the Pre-trained Model to Generate Simple Texts for Text Simplification](https://mlsanity.com/paper/2305.12463/) (2023)
- [On the Robustness of Editing Large Language Models](https://mlsanity.com/paper/2402.05827/) (2024)
- [DocEdit-v2: Document Structure Editing Via Multimodal LLM Grounding](https://mlsanity.com/paper/2410.16472/) (2024)
- [Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule](https://mlsanity.com/paper/2311.11813/) (2023)

## Built on by (most cited first, 21 of 21)

- [Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views](https://mlsanity.com/paper/2510.18632/) (2025)
- [HUD: Hierarchical Uncertainty-Aware Disambiguation Network for Composed Video Retrieval](https://mlsanity.com/paper/2512.02792/) (2025)
- [INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval](https://mlsanity.com/paper/2604.18051/) (2026)
- [SIFThinker: Spatially-Aware Image Focus for Visual Reasoning](https://mlsanity.com/paper/2508.06259/) (2025)
- [PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization](https://mlsanity.com/paper/2509.12446/) (2025)
- [Mano Technical Report](https://mlsanity.com/paper/2509.17336/) (2025)
- [TreeDiff: AST-Guided Code Generation with Diffusion LLMs](https://mlsanity.com/paper/2508.01473/) (2025)
- [Neural-Driven Image Editing](https://mlsanity.com/paper/2507.05397/) (2025)
- [Urban-MAS: Human-Centered Urban Prediction with LLM-Based Multi-Agent System](https://mlsanity.com/paper/2511.00096/) (2025)
- [HyperEdit: Unlocking Instruction-based Text Editing in LLMs via Hypernetworks](https://mlsanity.com/paper/2512.12544/) (2025)
- [Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization](https://mlsanity.com/paper/2607.08057/) (2026)
- [Attribution-Guided Model Rectification of Unreliable Neural Network Behaviors](https://mlsanity.com/paper/2603.15656/) (2026)
- [Memory-Aware and Uncertainty-Guided Retrieval for Multi-Hop Question Answering](https://mlsanity.com/paper/2503.23095/) (2025)
- [MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis](https://mlsanity.com/paper/2604.11188/) (2026)
- [Security--Fidelity Tradeoffs: The Hidden Cost of Prompt Injection Defense](https://mlsanity.com/paper/2606.30783/) (2026)
- [Agora: Toward Autonomous Bug Detection in Production-Level Consensus Protocols with LLM Agents](https://mlsanity.com/paper/2605.29910/) (2026)
- [CDR-Bench: Evaluating Faithful Execution of Compositional, Order-Sensitive Data Refinement Recipes](https://mlsanity.com/paper/2606.31435/) (2026)
- [AI Application in Anti-Money Laundering for Sustainable and Transparent Financial Systems](https://mlsanity.com/paper/2512.06240/) (2025)
- [Slang Context-based Inference Enhancement via Greedy Search-Guided Chain-of-Thought Prompting](https://mlsanity.com/paper/2603.13230/) (2026)
- [How Should Diffusion Language Models Edit Code?](https://mlsanity.com/paper/2609.38257/) (2026)
- [Can LLMs Separate Pasted Artifacts from User Speech? Absorption at Unmarked Prompt Seams](https://mlsanity.com/paper/2610.04210/) (2026)

## Abstract (arXiv)

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/FineEditbench.