New method quantifies LLM uncertainty using semantic entropy and conformal calibration

Researchers have developed a new method called Adaptive Conformal Semantic Entropy (ACSE) to better estimate the uncertainty of Large Language Models (LLMs). This approach focuses on the semantic dispersion of different responses to the same prompt, rather than just lexical or probabilistic measures. ACSE adaptively adjusts uncertainty scores based on semantic features and uses conformal calibration to ensure statistical reliability, bounding the error rate of accepted responses. Experiments show ACSE significantly outperforms existing methods, achieving an AUROC of 0.88 on the TriviaQA dataset compared to 0.65 for token entropy. AI

IMPACT Improves reliability of LLMs in safety-critical applications by providing better uncertainty estimates.

RANK_REASON Academic paper introducing a novel method for LLM uncertainty quantification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Adaptive Conformal Semantic Entropy
• arXiv
• LLMs
• TriviaQA

• paper
• safety

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