Two calls per example identify the first two moments of latent correctness probability, enabling exact bounds on the vote-accuracy curve for any majority-vote budget under conditional i.i.d. assumptions.
Estimating the self-consistency of LLMs,
2 Pith papers cite this work. Polarity classification is still indexing.
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SFL-MTSC improves slot F1 and overall accuracy in zero-shot multi-intent SLU on MAC-SLU by decomposing predictions into intent-specific frames, applying domain-intent grouping and slot clustering, and retaining reliable frames via path support scoring.
citing papers explorer
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Two Calls, Two Moments, and the Vote-Accuracy Curve of Repeated LLM Inference
Two calls per example identify the first two moments of latent correctness probability, enabling exact bounds on the vote-accuracy curve for any majority-vote budget under conditional i.i.d. assumptions.
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SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding
SFL-MTSC improves slot F1 and overall accuracy in zero-shot multi-intent SLU on MAC-SLU by decomposing predictions into intent-specific frames, applying domain-intent grouping and slot clustering, and retaining reliable frames via path support scoring.