Code Comparison Tuning for Code Large Language Models

• URL: http://arxiv.org/abs/2403.19121v2
• Date: Wed, 5 Jun 2024 04:00:08 GMT
• Title: Code Comparison Tuning for Code Large Language Models
• Authors: Yufan Jiang, Qiaozhi He, Xiaomin Zhuang, Zhihua Wu,
• Abstract summary: We present Code Comparison Tuning (CCT), a simple and effective tuning method for code large language models (Code LLMs) CCT integrates the concept of comparison into instruction tuning, both at the token and sequence levels. We show that CCT surpasses instruction tuning in pass@1 scores by up to 4 points across diverse code LLMs.
• Score: 7.03872473285061
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Code Comparison Tuning (CCT), a simple and effective tuning method for code large language models (Code LLMs) to better handle subtle code errors. Specifically, we integrate the concept of comparison into instruction tuning, both at the token and sequence levels, enabling the model to discern even the slightest deviations in code. To compare the original code with an erroneous version containing manually added code errors, we use token-level preference loss for detailed token-level comparisons. Additionally, we combine code segments to create a new instruction tuning sample for sequence-level comparisons, enhancing the model's bug-fixing capability. Experimental results on the HumanEvalFix benchmark show that CCT surpasses instruction tuning in pass@1 scores by up to 4 points across diverse code LLMs, and extensive analysis demonstrates the effectiveness of our method.

Related papers

• Enhancing High-Quality Code Generation in Large Language Models with Comparative Prefix-Tuning [19.53507218261719]
  Large Language Models (LLMs) have been widely adopted in commercial code completion engines. LLMs may generate code with quality issues that violate coding standards. We propose a novel comparative prefix-tuning method for controllable high-quality code generation.
  arXiv  Detail & Related papers  (2025-03-12T03:15:46Z)
• OASIS: Order-Augmented Strategy for Improved Code Search [14.15428333014365]
  We propose a novel order-augmented strategy for improved code search (OASIS) It leverages order-based similarity labels to train models to capture subtle differences in similarity among negative pairs. It underscores the value of exploiting subtle differences among negative pairs with order labels for effective code embedding training.
  arXiv  Detail & Related papers  (2025-03-11T08:26:37Z)
• Robust Learning of Diverse Code Edits [10.565439872488328]
  Software engineering activities frequently involve edits to existing code. Code language models (LMs) lack the ability to handle diverse types of code-edit requirements.
  arXiv  Detail & Related papers  (2025-03-05T16:39:04Z)
• KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding [49.56049319037421]
  KodCode is a synthetic dataset that addresses the persistent challenge of acquiring high-quality, verifiable training data. It comprises question-solution-test triplets that are systematically validated via a self-verification procedure. This pipeline yields a large-scale, robust and diverse coding dataset.
  arXiv  Detail & Related papers  (2025-03-04T19:17:36Z)
• IterPref: Focal Preference Learning for Code Generation via Iterative Debugging [28.020886216989872]
  We propose IterPref, a new preference alignment framework for Code LLMs. IterPref explicitly locates error regions and aligns the corresponding tokens via a tailored DPO algorithm. IterPref achieves significant performance gains in code generation and improves on challenging tasks like BigCodeBench.
  arXiv  Detail & Related papers  (2025-03-04T16:56:34Z)
• Rethinking Code Refinement: Learning to Judge Code Efficiency [60.04718679054704]
  Large Language Models (LLMs) have demonstrated impressive capabilities in understanding and generating codes. We propose a novel method based on the code language model that is trained to judge the efficiency between two different codes. We validate our method on multiple programming languages with multiple refinement steps, demonstrating that the proposed method can effectively distinguish between more and less efficient versions of code.
  arXiv  Detail & Related papers  (2024-10-29T06:17:37Z)
• Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting [78.48355455324688]
  We propose a novel zero-shot synthetic code detector based on the similarity between the code and its rewritten variants. Our results demonstrate a notable enhancement over existing synthetic content detectors designed for general texts.
  arXiv  Detail & Related papers  (2024-05-25T08:57:28Z)
• 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)
• Contrastive Instruction Tuning [61.97704869248903]
  We propose Contrastive Instruction Tuning to maximize the similarity between semantically equivalent instruction-instance pairs. Experiments on the PromptBench benchmark show that CoIN consistently improves LLMs' robustness to unseen instructions with variations across character, word, sentence, and semantic levels by an average of +2.5% in accuracy.
  arXiv  Detail & Related papers  (2024-02-17T00:09:32Z)
• DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning [36.78560777629329]
  We introduce a diverse instruction model (DolphCoder) with self-evaluating for code generation. It learns diverse instruction targets and combines a code evaluation objective to enhance its code generation ability. Our model achieves superior performance on the HumanEval and MBPP benchmarks.
  arXiv  Detail & Related papers  (2024-02-14T12:34:58Z)
• Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding [46.485363806259265]
  Speculative Decoding has emerged as a novel decoding paradigm for Large Language Models (LLMs) inference. In each decoding step, this method first drafts several future tokens efficiently and then verifies them in parallel. This paper presents a comprehensive overview and analysis of this promising decoding paradigm.
  arXiv  Detail & Related papers  (2024-01-15T17:26:50Z)
• CodeRetriever: Unimodal and Bimodal Contrastive Learning [128.06072658302165]
  We propose the CodeRetriever model, which combines the unimodal and bimodal contrastive learning to train function-level code semantic representations. For unimodal contrastive learning, we design a semantic-guided method to build positive code pairs based on the documentation and function name. For bimodal contrastive learning, we leverage the documentation and in-line comments of code to build text-code pairs.
  arXiv  Detail & Related papers  (2022-01-26T10:54:30Z)
• CLSEBERT: Contrastive Learning for Syntax Enhanced Code Pre-Trained Model [23.947178895479464]
  We propose CLSEBERT, a Constrastive Learning Framework for Syntax Enhanced Code Pre-Trained Model. In the pre-training stage, we consider the code syntax and hierarchy contained in the Abstract Syntax Tree (AST) We also introduce two novel pre-training objectives. One is to predict the edges between nodes in the abstract syntax tree, and the other is to predict the types of code tokens.
  arXiv  Detail & Related papers  (2021-08-10T10:08:21Z)
• GraphCodeBERT: Pre-training Code Representations with Data Flow [97.00641522327699]
  We present GraphCodeBERT, a pre-trained model for programming language that considers the inherent structure of code. We use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "where-the-value-comes-from" between variables. We evaluate our model on four tasks, including code search, clone detection, code translation, and code refinement.
  arXiv  Detail & Related papers  (2020-09-17T15:25:56Z)

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.