Characterizing Overconfident Failure in LLM-Based Code Generation
Quick summary
arXiv:2610.11300v1 Announce Type: cross Abstract: 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 con
Key takeaways
- arXiv:2610.11300v1 Announce Type: cross Abstract: 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.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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