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Thesis: LLM hidden states can predict code correctness

By PulseAugur Editorial · [1 sources] ·

A new thesis explores the use of Introspective Uncertainty Estimation (IUE) to gauge the correctness of code generated by Large Language Models (LLMs). The research indicates that LLM hidden states can effectively signal functional code correctness at both the response and line levels, which is crucial for practical software engineering. While static single-token probes proved most effective, generalization across different tasks and domains showed some degradation. The study also found that line-level prediction is significantly more challenging than response-level estimation, though a conditional localization setup demonstrated effectiveness in identifying points of failure. AI

IMPACT This research could improve the reliability and trustworthiness of LLM-generated code in practical software development.

RANK_REASON Academic paper detailing a novel method for evaluating LLM code generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Thesis: LLM hidden states can predict code correctness

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Academic paper detailing a novel method for evaluating LLM code generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Thomas Klassert ·

    Introspective Uncertainty Estimation for LLM-Based Code Generation

    arXiv:2609.13975v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for code generation but can produce fluent yet functionally incorrect outputs, which limits trust in their usage for practical software engineering workflows. This thesis investig…

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