LLMs Uncertainty Quantification via Adaptive Conformal Semantic Entropy

Researchers propose Adaptive Conformal Semantic Entropy (ACSE), a novel method for quantifying uncertainty in large language model outputs by measuring semantic diversity rather than relying solely on lexical or probabilistic measures. The approach uses conformal calibration to provide statistical guarantees on error rates, demonstrating significant performance improvements over existing uncertainty quantification baselines.

Large language models frequently suffer from overconfidence, particularly when generating hallucinations—false information presented with unwarranted certainty. This vulnerability creates critical safety risks in high-stakes applications like medical diagnosis, legal analysis, and financial advice. The paper addresses this fundamental challenge by introducing ACSE, which measures uncertainty at the semantic level rather than treating all responses with similar meanings as distinct outputs.

Traditional uncertainty quantification methods analyze token-level probabilities or surface-level lexical diversity, missing an important layer of analysis: whether different outputs convey equivalent meaning. ACSE clusters semantic representations of multiple model responses and adaptively calibrates uncertainty scores based on cluster characteristics. By incorporating conformal prediction theory, the method provides distribution-free guarantees that error rates among accepted responses remain below user-specified thresholds—a crucial property for safety-critical deployments.

The experimental results demonstrate substantial performance gains. On TriviaQA, ACSE achieves 0.88 AUROC compared to 0.65 for token entropy, representing a 35% relative improvement. This advancement matters for practitioners deploying LLMs in production environments where understanding model reliability determines whether systems can be trusted or require additional human review.

The work signals broader momentum in making LLMs more reliable through better uncertainty estimation. As enterprises increasingly adopt these models for mission-critical tasks, uncertainty quantification becomes a core infrastructure concern. Future research will likely focus on computational efficiency and integration with existing model deployment pipelines.

• →ACSE measures semantic rather than lexical uncertainty, improving detection of when LLMs hallucinate despite confidence signals.
• →Conformal calibration provides finite-sample, distribution-free guarantees on error rates, enabling safer deployment decisions.
• →Performance improvements exceed 30% on benchmark datasets compared to existing uncertainty quantification methods.
• →The approach adaptively adjusts scoring based on semantic cluster features, making it more nuanced than fixed probability thresholds.
• →Better uncertainty quantification addresses a critical bottleneck for enterprise LLM adoption in safety-sensitive domains.