| Challenge: | Large language models can modify JSON documents through natural language commands . current approaches regenerate entire structures for each edit, consuming computational resources . |
| Approach: | They propose a framework that enables large language models to generate diff patches instead of complete documents. |
| Outcome: | The proposed framework reduces token usage by 31% while maintaining edit quality within 5% of full regeneration. |
Similar Papers
Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study shows that large language models can perform precise text editing tasks. |
| Approach: | InstrEditBench is a benchmark dataset that compares 30,000 structured editing tasks . experimental evaluations show FineEdit outperforms state-of-the-art models . |
| Outcome: | The proposed model outperforms state-of-the-art models on single-turn edits and mistral-7B-OpenOrca on direct edits. |
Knowledge Editing for Large Language Models (2024.lrec-tutorials)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are not immune to issues of factual accuracy or logically consistent. |
| Approach: | This tutorial will present cutting-edge methods and practical tools for editing Large Language Models (LLMs). |
| Outcome: | The aim of this course is to familiarize researchers with the latest advancements and emerging strategies in the realm of knowledge editing for LLMs. |
Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models have demonstrated considerable success in various natural language processing tasks, but their performance in NMT tasks is still underexplored. |
| Approach: | They propose to use LLMs as automatic post-editors rather than direct translators to improve BLEU and COMET performance. |
| Outcome: | The proposed approach improves BLEU but COMET performance compared to in-context learning. |
JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing text-to-text methods struggle with issues such as generalization, robustness, and controllability due to their lack of explicit task structures. |
| Approach: | They propose a structure-to-structure approach that uses JSON structures to represent tasks. |
| Outcome: | The proposed method outperforms TextTuning in terms of performance, robustness, and controllability across different scenarios. |
Self-Edit: Fault-Aware Code Editor for Code Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing Large language models (LLMs) have low pass rates and accuracy on competitive programming tasks. |
| Approach: | They propose a generate-and-edit approach that uses execution results of generated code from LLMs to improve code quality on competitive programming tasks. |
| Outcome: | The proposed method improves pass@1 by 89% on APPS-dev, 31% on apps-test, and 48% on HumanEval over nine popular code generation LLMs with parameter sizes ranging from 110M to 175B. |
InstructCoder: Instruction Tuning Large Language Models for Code Editing (2024.acl-srw)
Copied to clipboard
| Challenge: | InstructCoder is the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing. |
| Approach: | They propose to use Large Language Models to edit code based on user instructions . they use a dataset to adapt LLMs to general-purpose code editing . |
| Outcome: | The proposed model can significantly improve code editing performance compared to proprietary models . the proposed model is based on a human-written execution-based benchmark . |
Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods to improve robustness require changing the fine-tuning process or large-scale data augmentation, which are infeasible or cost prohibitive for closed-source models. |
| Approach: | They propose to prioritize more complex examples or replace existing training examples with LLM-generated data to improve performance on OOD NLI datasets. |
| Outcome: | The proposed methods improve performance on difficult OOD datasets while training with synthetic data leads to substantial improvements on easier OOD data. |
Can docstring reformulation with an LLM improve code generation? (2024.eacl-srw)
Copied to clipboard
| Challenge: | Existing approaches focus on training, fine-tuning or prompting LLMs to generate better outputs given the same input. |
| Approach: | They propose to optimize part of the input, the docstring, via reformulation with an LLM to improve code generation. |
| Outcome: | The proposed methods improve code generation on the original HumanEval benchmark and multiple curated variants on the same input. |
On the Robustness of Editing Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have exhibited impressive success and significant potential. |
| Approach: | They propose to modify the knowledge memory with minimum computational cost while preserving the performance on the retained knowledge. |
| Outcome: | The proposed methods avoid retraining to update the model parameters and have demonstrated promising performance and efficiency. |
Bayesian Optimization for Controlled Image Editing via LLMs (2025.findings-acl)
Copied to clipboard
Chengkun Cai, Haoliang Liu, Xu Zhao, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, John Lee, Jenq-Neng Hwang, Lei Li
| Challenge: | achieving precise control over generated content and maintaining semantic consistency remain significant limitations, particularly concerning grounding techniques and the necessity for model fine-tuning. |
| Approach: | They propose an off-the-shelf approach that integrates Large Language Models with Bayesian Optimization to facilitate precise and user-friendly image editing. |
| Outcome: | The proposed approach outperforms existing methods in editing accuracy and semantic preservation, as validated using different LLMs including Claude3 and GPT-4. |