Confident in a Confidence Score: Investigating the Sensitivity of Confidence Scores to Supervised Fine-Tuning
Factual evidence
What the source reports
Research finds supervised fine-tuning (SFT) can decorrelate LLM confidence scores from output quality, impairing uncertainty quantification.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 22 September 2026
- Collected by OneBench
- 13 Apr 2026, 15:28 UK
Stored source excerpt
arXiv:2604.08974v1 Announce Type: new Abstract: Uncertainty quantification is a set of techniques that measure confidence in language models. They can be used, for example, to…
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OneBench interpretation
Institutional assessment
So what
This research confirms that standard fine-tuning practices directly undermine the reliability of confidence scores used for critical model risk mitigation, such as hallucination detection.
Do what
Your model validation framework must explicitly test the correlation between confidence scores and output quality for any fine-tuned models before production deployment.