LLM-Assisted Code Cleaning For Training Accurate Code Generators

• URL: http://arxiv.org/abs/2311.14904v1
• Date: Sat, 25 Nov 2023 02:45:50 GMT
• Title: LLM-Assisted Code Cleaning For Training Accurate Code Generators
• Authors: Naman Jain, Tianjun Zhang, Wei-Lin Chiang, Joseph E. Gonzalez, Koushik Sen, Ion Stoica
• Abstract summary: We investigate data quality for code and find that making the code more structured and readable leads to improved code generation performance of the system. We build a novel data-cleaning pipeline that uses these principles to transform existing programs. We evaluate our approach on two challenging algorithmic code generation benchmarks and find that fine-tuning CodeLLaMa-7B improves the performance by up to 30% compared to fine-tuning on the original dataset.
• Score: 53.087019724256606
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Natural language to code generation is an important application area of LLMs and has received wide attention from the community. The majority of relevant studies have exclusively concentrated on increasing the quantity and functional correctness of training sets while disregarding other stylistic elements of programs. More recently, data quality has garnered a lot of interest and multiple works have showcased its importance for improving performance. In this work, we investigate data quality for code and find that making the code more structured and readable leads to improved code generation performance of the system. We build a novel data-cleaning pipeline that uses these principles to transform existing programs by 1.) renaming variables, 2.) modularizing and decomposing complex code into smaller helper sub-functions, and 3.) inserting natural-language based plans via LLM based transformations. We evaluate our approach on two challenging algorithmic code generation benchmarks and find that fine-tuning CodeLLaMa-7B on our transformed modularized programs improves the performance by up to 30% compared to fine-tuning on the original dataset. Additionally, we demonstrate improved performance from using a smaller amount of higher-quality data, finding that a model fine-tuned on the entire original dataset is outperformed by a model trained on 15% of our cleaned dataset. Even in comparison to closed-source models, our models outperform the much larger AlphaCoder models.

Related papers

• OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models [70.72097493954067]
  Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning, tasks and agent systems. We introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an open cookbook'' for the research community.
  arXiv  Detail & Related papers  (2024-11-07T17:47:25Z)
• What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
  Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions. We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types. We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
  arXiv  Detail & Related papers  (2024-07-08T17:27:17Z)
• Code Less, Align More: Efficient LLM Fine-tuning for Code Generation with Data Pruning [4.975728472540823]
  We present techniques that integrate various clustering and pruning metrics to selectively reduce training data without compromising the accuracy and functionality of the generated code. Our experiments show that these pruning strategies not only reduce the computational resources needed but also enhance the overall quality code generation.
  arXiv  Detail & Related papers  (2024-07-06T10:30:43Z)
• UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback [21.858896845159208]
  Large language models (LLMs) struggle to consistently generate UI code that compiles and produces visually relevant designs. Existing approaches to improve generation rely on expensive human feedback or distilling a proprietary model. Our method starts with an existing LLM and iteratively produces improved models by self-generating a large synthetic dataset.
  arXiv  Detail & Related papers  (2024-06-11T21:53:46Z)
• 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)
• Performance-Aligned LLMs for Generating Fast Code [2.180216161965907]
  We introduce a reinforcement learning based methodology to align the outputs of code LLMs with performance. We demonstrate that our fine-tuned model improves the expected speedup of generated code over base models for a set of benchmark tasks.
  arXiv  Detail & Related papers  (2024-04-29T16:52:38Z)
• Code Needs Comments: Enhancing Code LLMs with Comment Augmentation [91.52444946362547]
  We introduce a novel data augmentation method that generates comments for existing code, coupled with a data filtering strategy that filters out code data poorly correlated with natural language. We conducted experiments on three code-focused Large Language Models and observed consistent improvements in performance on two widely-used programming skill benchmarks.
  arXiv  Detail & Related papers  (2024-02-20T13:56:38Z)
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback [58.20547418182074]
  We introduce StepCoder, a novel framework for code generation, consisting of two main components. CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks. FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization. Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
  arXiv  Detail & Related papers  (2024-02-02T13:14:31Z)

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.