AIBullisharXiv – CS AI · 3h ago7/10
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Localizing Input Uncertainty Quantification for Large Language Models via Shapley Values
Researchers introduce ShaQ, a Shapley-value-based framework that identifies which specific parts of user input cause uncertainty in large language models, rather than just flagging overall uncertainty. The method achieves state-of-the-art ambiguity detection on multiple benchmarks and demonstrates practical value in high-stakes domains like clinical settings by enabling targeted input clarification.