InstructCoder: Instruction Tuning Large Language Models for Code Editing

• URL: http://arxiv.org/abs/2310.20329v3
• Date: Wed, 28 Feb 2024 15:47:11 GMT
• Title: InstructCoder: Instruction Tuning Large Language Models for Code Editing
• Authors: Kaixin Li, Qisheng Hu, Xu Zhao, Hui Chen, Yuxi Xie, Tiedong Liu, Qizhe Xie, Junxian He
• Abstract summary: We explore the use of Large Language Models (LLMs) to edit code based on user instructions. InstructCoder is the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing. Our findings reveal that open-source LLMs fine-tuned on InstructCoder can significantly enhance the accuracy of code edits.
• Score: 26.160498475809266
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Code editing encompasses a variety of pragmatic tasks that developers deal with daily. Despite its relevance and practical usefulness, automatic code editing remains an underexplored area in the evolution of deep learning models, partly due to data scarcity. In this work, we explore the use of Large Language Models (LLMs) to edit code based on user instructions. Evaluated on a novel human-written execution-based benchmark dubbed EditEval, we found current models often struggle to fulfill the instructions. In light of this, we contribute InstructCoder, the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing, containing high-diversity code-editing tasks such as comment insertion, code optimization, and code refactoring. It consists of over 114,000 instruction-input-output triplets and covers multiple distinct code editing scenarios. The collection process starts with filtered commit data sourced from GitHub Python repositories as seeds. Subsequently, the dataset is systematically expanded through an iterative process, where both seed and generated tasks are used to prompt ChatGPT for more data. Our findings reveal that open-source LLMs fine-tuned on InstructCoder can significantly enhance the accuracy of code edits, exhibiting superior code-editing performance matching advanced proprietary LLMs. The datasets and the source code are publicly available at https://github.com/qishenghu/CodeInstruct.

Related papers

• VersiCode: Towards Version-controllable Code Generation [58.82709231906735]
  Large Language Models (LLMs) have made tremendous strides in code generation, but existing research fails to account for the dynamic nature of software development. We propose two novel tasks aimed at bridging this gap: version-specific code completion (VSCC) and version-aware code migration (VACM) We conduct an extensive evaluation on VersiCode, which reveals that version-controllable code generation is indeed a significant challenge.
  arXiv  Detail & Related papers  (2024-06-11T16:15:06Z)
• AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data [64.69872638349922]
  We present AlchemistCoder, a series of Code LLMs with enhanced code generation and generalization capabilities fine-tuned on multi-source data. We propose incorporating the data construction process into the fine-tuning data as code comprehension tasks, including instruction evolution, data filtering, and code review.
  arXiv  Detail & Related papers  (2024-05-29T16:57:33Z)
• CodecLM: Aligning Language Models with Tailored Synthetic Data [51.59223474427153]
  We introduce CodecLM, a framework for adaptively generating high-quality synthetic data for instruction-following abilities. We first encode seed instructions into metadata, which are concise keywords generated on-the-fly to capture the target instruction distribution. We also introduce Self-Rubrics and Contrastive Filtering during decoding to tailor data-efficient samples.
  arXiv  Detail & Related papers  (2024-04-08T21:15:36Z)
• CodeEditorBench: Evaluating Code Editing Capability of Large Language Models [49.387195629660994]
  Large Language Models (LLMs) for code are rapidly evolving, with code editing emerging as a critical capability. We introduce CodeEditorBench, an evaluation framework designed to rigorously assess the performance of LLMs in code editing tasks. We curate diverse coding challenges and scenarios from five sources, covering various programming languages, complexity levels, and editing tasks.
  arXiv  Detail & Related papers  (2024-04-04T15:49:49Z)
• WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning [22.44573249705913]
  We present WaveCoder, a series of Code LLMs trained with Widespread And Versatile Enhanced instruction data. To enable the models to tackle complex code-related tasks, we propose a method to stably generate diverse, high-quality instruction data from open source code dataset. Our experiments demonstrate that WaveCoder models significantly outperform other open-source models in terms of the generalization ability across different code-related tasks.
  arXiv  Detail & Related papers  (2023-12-20T09:02:29Z)
• Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions [6.367360745627828]
  We introduce a benchmark of code editing tasks and use it to evaluate several cutting edge LLMs. Our evaluation exposes a significant gap between the capabilities of state-of-the-art open and closed models. We introduce a new, carefully curated, permissively licensed training dataset of code editing tasks coupled with natural language instructions.
  arXiv  Detail & Related papers  (2023-12-11T02:27:45Z)
• Coeditor: Leveraging Contextual Changes for Multi-round Code Auto-editing [57.776971051512234]
  In this work, we explore a multi-round code auto-editing setting, aiming to predict edits to a code region based on recent changes within the same. Our model, Coeditor, is a fine-tuned language model specifically designed for code editing tasks. In a simplified single-round, single-edit task, Coeditor significantly outperforms GPT-3.5 and SOTA open-source code completion models.
  arXiv  Detail & Related papers  (2023-05-29T19:57:36Z)
• GrACE: Generation using Associated Code Edits [23.643567386291988]
  We endowing pre-trained large language models (LLMs) of code with the knowledge of prior, relevant edits. The generative capability of the LLMs helps address the diversity in code changes and conditioning code generation on prior edits. We evaluate two well-known LLMs, Codex and CodeT5, in zero-shot and fine-tuning settings respectively.
  arXiv  Detail & Related papers  (2023-05-23T14:55:44Z)
• CodeT5+: Open Code Large Language Models for Code Understanding and Generation [72.1638273937025]
  Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. CodeT5+ is a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of downstream code tasks. We extensively evaluate CodeT5+ on over 20 code-related benchmarks in different settings, including zero-shot, finetuning, and instruction-tuning.
  arXiv  Detail & Related papers  (2023-05-13T14:23:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.