Rethinking Repetition Problems of LLMs in Code Generation

TL;DR

This paper addresses structural repetition in code generated by LLMs by introducing RPG, a grammar-based decoding method that reduces repetitions and improves code quality, supported by a new evaluation dataset and extensive experiments.

Contribution

The paper formally defines structural repetition in code generation and proposes RPG, a novel grammar-based decoding approach to mitigate this issue in LLMs.

Findings

RPG significantly reduces structural repetitions in generated code.

RPG outperforms existing baselines on multiple benchmarks.

The new dataset CodeRepetEval enables comprehensive evaluation of repetition mitigation methods.

Abstract

With the advent of neural language models, the performance of code generation has been significantly boosted. However, the problem of repetitions during the generation process continues to linger. Previous work has primarily focused on content repetition, which is merely a fraction of the broader repetition problem in code generation. A more prevalent and challenging problem is structural repetition. In structural repetition, the repeated code appears in various patterns but possesses a fixed structure, which can be inherently reflected in grammar. In this paper, we formally define structural repetition and propose an efficient decoding approach called RPG, which stands for Repetition Penalization based on Grammar, to alleviate the repetition problems in code generation for LLMs. Specifically, RPG first leverages grammar rules to identify repetition problems during code generation, and then strategically decays the likelihood of critical tokens that contribute to repetitions, thereby mitigating them in code generation. To facilitate this study, we construct a new dataset CodeRepetEval to comprehensively evaluate approaches for mitigating the repetition problems in code generation. Extensive experimental results demonstrate that RPG substantially outperforms the best-performing baselines on CodeRepetEval dataset as well as HumanEval and MBPP benchmarks, effectively reducing repetitions and enhancing the quality of generated code.