Characterizing Overconfident Failure in LLM-Based Code Generation
Read the original on arXiv AI →The paper investigates how large language models (LLMs) used for code generation can produce syntactically plausible but incorrect programs, a phenomenon termed overconfident failure. By evaluating four open-source code models across three execution-based benchmarks, the authors examine whether token-level uncertainty metrics can predict execution correctness, analyze overconfidence at both global and local levels, and test mitigation strategies such as selective generation and instruction tuning. Their findings show that existing uncertainty signals are only partially reliable, overconfidence persists across models, instruction tuning can increase certainty without improving correctness discrimination, and common mitigation techniques do not fully resolve the issue. "whyItMatters":"The study highlights that current uncertainty metrics and mitigation strategies are insufficient to reliably detect or prevent incorrect code generation, underscoring the need for better reliability signals in LLM-based code synthesis."
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