Fine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revision
In today's AI-assisted software engineering landscape, developers increasingly depend on LLMs that are highly capable, yet inherently imperfect. The tendency of these models to produce incorrect outputs can reduce developer productivity. To this end, a canonical mitigation method is to provide calibrated confidence scores that faithfully reflect their likelihood of correctness at the instance-level. Such information allows users to make immediate decisions regarding output acceptance, abstain error-prone outputs, and better align their expectations with the model's capabilities. Since post-trained LLMs do not inherently produce well-calibrated confidence scores, researchers have developed...
| Authors | Hong Yi Lin, Chunhua Liu, Haoyu Gao et al. |
|---|---|
| Primary category | cs.SE |
| Published | 8 April 2026 |
| Last updated at source | 15 September 2026 |
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