arXiv:2605.04295v2 Announce Type: replace Abstract: LLMs' overconfidence, particularly when hallucinating, poses a significant challenge for the deployment of the models in safety-critical settings and makes a reliable estimation of uncertainty necessary. Existing approaches for uncertainty quantification typically prioritize lexical or probabilistic measures; however, these techniques often ignore the semantic variance of different responses with similar meaning. In this paper, we propose Adaptive Conformal Semantic Entropy (ACSE), a method for estimating prompt-level uncertainty by adaptivel
The increasing deployment of LLMs in critical applications necessitates robust uncertainty quantification methods to address overconfidence and hallucination concerns, which existing methods often fail to fully capture semantically.
This development is crucial for advancing the reliability and trustworthiness of AI systems deployed in sensitive environments, directly impacting their commercial viability and regulatory acceptance.
The proposed ACSE method introduces a novel approach to uncertainty quantification that focuses on semantic variance, potentially leading to more accurate and dependable LLM outputs beyond mere lexical or probabilistic measures.
- · AI developers
- · Safety-critical industries (e.g., healthcare, autonomous driving)
- · Regulatory bodies
- · Developers relying solely on traditional uncertainty metrics
More widespread and confident deployment of LLMs in applications requiring high reliability.
Increased investor confidence and public trust in AI technologies as their outputs become more auditable and predictable.
The acceleration of AI adoption paradigms where human-level certainty is a prerequisite, potentially transforming professional knowledge work.
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Read at arXiv cs.LG