AGORA: Reliability-Aware Aggregation of LLM Judges via Item-Response Theory

Authors: Junhyung Ahn

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Abstract: LLM-as-a-judge is a common substitute for costly human evaluation, and aggregating several judges is the standard remedy for any single judge's bias. Judge pools mix judges of very different reliability, while the human-labeled sets that validate them hold a few hundred items or fewer. In this regime the per-item mean weights weak judges as heavily as strong ones, and confusion-table models such as Dawid-Skene must estimate a per-judge error table from too few items. We propose AGORA (Aggregation Of Reliability-weighted Assessments), an item-response-theory aggregator that places every item on one latent quality scale and describes each judge by a sensitivity parameter and ordered rating thresholds, so judge reliability is learned from all items jointly and without human labels. Across three benchmarks, AGORA matches or exceeds Dawid-Skene on large evaluation sets, and its advantage widens as the set shrinks toward a few hundred items. It remains accurate as pools grow more uneven, a label-free dispersion statistic indicates in advance when trust weighting helps, and its learned trust weights track per-judge human validity.

Subjects:

Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL)

Cite as:

arXiv:2611.05556 [cs.AI]

(or

arXiv:2611.05556v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.05556

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arXiv-issued DOI via DataCite (pending registration)

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From: Junhyung Ahn [ view email ]

[v1]

Tue, 3 Nov 2026 19:49:01 UTC (131 KB)

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