LLM Evaluation as Tensor Completion: Low Rank Structure and Semiparametric Efficiency
Large language model (LLM) evaluation platforms increasingly rely on pairwise human judgments. These data are noisy, sparse, and non-uniform, yet leaderboards are reported with limited uncertainty quantification. We study this as semiparametric inference for a low-rank latent score tensor observed through pairwise comparisons under Bradley-Terry-Luce-type models. This places LLM evaluation in a new tensor completion setting with structured observations, non-uniform sampling, and pairwise contrasts. Our target is a smooth functional $ψ(T^\star)$, including linear estimands such as ability gaps and nonlinear ones such as win probabilities. We derive the information operator on the low-rank ...
| Authors | Jiachun Li, David Simchi-Levi, Will Wei Sun |
|---|---|
| Primary category | stat.ME |
| Published | 7 April 2026 |
| Last updated at source | 3 September 2026 |
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