Calibrating Overconfidence Without Sacrificing Confidence: Probe-Conditioned Head Intervention for LLMs
arXiv:2606.09876v1 Announce Type: new Abstract: Large language models often express high confidence in answers that are wrong. Standard calibration remedies typically act globally or at the score level, reducing unwarranted confidence but also risking erosion of warranted confidence on correct answers. We introduce Probe-Conditioned Head Intervention (PCHI), an inference-time method that uses a frozen probe to detect likely wrong-but-confident responses and conditionally rescales downstream attention-head outputs during confidence generation. On Qwen3-4B-Instruct solving OpenMathInstruct probl
The proliferation of advanced LLMs highlights the critical need for improving their reliability and mitigating 'hallucination' issues, especially as they integrate into high-stakes applications.
Improving LLM calibration without sacrificing performance is crucial for their adoption in enterprise and mission-critical systems, directly impacting trust and utility for users.
The ability to fine-tune LLM confidence post-training without global impact represents a significant step towards more reliable and deployable AI systems.
- · AI developers
- · Enterprise AI adoption
- · LLM users
- · Models prone to overconfidence
- · Uncalibrated LLM applications
More trustworthy and effective large language models become available for integration into various products and services.
This improved reliability could accelerate the development and deployment of autonomous AI agents and critical decision-making systems.
Increased trust in AI outputs could lead to broader societal integration of AI, potentially transforming entire industries and reducing human oversight in certain domains.
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Read at arXiv cs.LG