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papersTODAY 04:00 UTC

Thesis Examines Introspective Uncertainty Estimation for LLM Code Generation

A newly posted arXiv thesis investigates whether large language models can gauge the reliability of the code they produce, addressing the problem of fluent but functionally incorrect output. The work focuses on introspective uncertainty estimation as a way to flag low-confidence generations in software engineering workflows. The abstract is truncated, so the full methods and results are not yet detailed in the listing.

arXivUncertainty estimationcode-generationintrospective uncertainty estimationlarge-language-modelssoftware-engineering

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