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Clustered Self-Assessment
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clustered-self-assessment-443f9cb2·1 events·first seen 13d agoAliases: Clustered Self-Assessment
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Clustered Self-Assessment: LLM uncertainty quantification via semantic clustering and multiple-choice self-evaluation
A new arXiv preprint proposes Clustered Self-Assessment, a method for uncertainty quantification in LLMs that groups sampled generations into semantically distinct clusters, reformats them as multiple-choice options, and uses the model's own probability assignments as confidence estimates. The approach outperforms entropy-based baselines across multiple models and datasets, achieving competitive performance with as few as two additional samples. The method is notable for directly leveraging the model's self-assessment capability rather than relying on indirect distributional signals.