Talking to Itself While Coding: What Makes Comments Help Code Generation?
Large Language Models (LLMs) often generate natural-language comments while writing code, and these comments become part of the context used to generate the code that follows. However, it remains unclear which properties of comments affect code-generation performance. We study this question through observational analyses and controlled interventions. On LiveCodeBench, neither comment frequency nor broad comment intent reliably predicts pass@1. We then prefill weaker recipient models with comment blocks written by stronger source models, allowing us to separate comment surface form from the solution content they convey. Comments from source solutions that pass the tests raise recipient pas...
| Authors | Dangfeng Pan, Zhensu Sun, Cenyuan Zhang et al. |
|---|---|
| Primary category | cs.SE |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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