STABLEVAL: Disagreement-Aware and Stable Evaluation of AI Systems
Human evaluation remains the primary standard for assessing modern AI systems, yet annotator disagreement, bias, and variability make system rankings fragile under standard majority vote aggregation. Majority vote discards annotator reliability and item-level ambiguity, often yielding unstable comparisons across annotator subsets. We introduce STABLEVAL, a disagreement-aware evaluation framework that models latent item correctness and annotator-specific confusion patterns to produce posterior expected item credit and calibrated agent-level scores. Unlike label-denoising approaches such as Dawid-Skene, STABLEVAL is explicitly designed for stable and uncertainty-aware system evaluation rath...
| Authors | Akash Bonagiri, Gerard Janno Anderias, Saee Patil et al. |
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| Primary category | cs.LG |
| Published | 4 May 2026 |
| Last updated at source | 1 June 2026 |
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