MUSE uses Jensen-Shannon divergence to pick a coherent subset of LLM predictions and averages them, improving binary-prediction calibration in several tasks.
InFindings of the Association for Computational Linguistics: EMNLP 2024, pages 2520–2537
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Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification
MUSE uses Jensen-Shannon divergence to pick a coherent subset of LLM predictions and averages them, improving binary-prediction calibration in several tasks.