Seongjun Lee
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126
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AI×1
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2026
Localizing Input Uncertainty Quantification for Large Language Models via Shapley Values
The paper proposes Shapley-based input uncertainty Quantification (ShaQ), a novel framework that uses Shapley values to precisely attribute input-induced uncertainty to specific spans of text, providing actionable guidance for clarification.
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