Characterizing Overconfident Failure in LLM-Based Code Generation
Large language models (LLMs) are increasingly used for automated code generation, but generated programs can appear syntactically plausible while still failing execution-based correctness checks. Existing validation methods, such as testing and program analysis, remain essential but are often incomplete, costly, or applied only after generation. Model-derived uncertainty is therefore a natural early reliability signal. This paper studies the dilemma of overconfidence in code LLMs where incorrect programs are often generated with token-level confidence comparable to correct programs. We study this dilemma across four open-source code models and three execution-based benchmarks. Our analysi...
| Authors | Ravishka Rathnasuriya, Wei Yang |
|---|---|
| Primary category | cs.SE |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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