Pairscore: scoring multiple items at once beats independent ranking for LLM uncertainty
yeewhye · x · 2026-10-03
Researcher haritv will present at COLM 2026, the Trustworthy AI workshop at the Simons Institute, and a frontier data summit in SF/Berkeley. The work, Pairscore, improves verbalised uncertainty in LLMs with a simple twist: asking models to score multiple items in one pass yields better calibration than independent scoring or pairwise comparisons/rankings. The author also showcases Paperena, an AI-driven tool to tackle the flood of papers.
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