arXiv:2606.05799v1 Announce Type: cross Abstract: Existing calibration methods for Large Language Models (LLMs) often overlook a critical dimension of trustworthiness: a model's {\em behavioral robustness} to irrelevant or misleading information. In this paper, we argue that a model's true confidence should reflect its stability under cognitive pressure. We introduce \textsc{CaliDist}, a novel post-hoc calibration approach that directly measures and penalizes a model's susceptibility to distraction. \textsc{CaliDist} quantifies how an LLM's predictions and uncertainty change when its input pro
The increasing sophistication and widespread deployment of LLMs necessitate more robust evaluation beyond accuracy, moving towards trustworthiness metrics like behavioral robustness.
Ensuring LLMs are not easily misled by irrelevant information is crucial for their reliable application in sensitive areas, enhancing their trustworthiness and reducing risks.
The focus of LLM calibration shifts from purely predictive confidence to a more comprehensive understanding that incorporates stability under cognitive pressure.
- · AI developers
- · LLM auditing firms
- · Enterprises deploying LLMs
- · Under-calibrated LLMs
- · Companies relying on unrobust LLMs
LLMs will become more reliable and less susceptible to adversarial attacks or misleading prompts.
New standards and benchmarks for LLM trustworthiness will emerge, driving further research into robust AI.
Public trust in AI systems could increase, accelerating adoption in critical sectors where reliability is paramount.
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Read at arXiv cs.CL