stat.AP · 2026-07-29 · No. 68
Applications, 2026-07-29.
1 new papers in stat.AP. Titles, authors,
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01 — The papers
1 entries-
01
Laplace-PSN-IRT: Uncertainty Quantification for Neural Item Response Theory Models of LLM Benchmarks
Juan Francisco, Mandujano Reyes
stat.AP · cs.AI · cs.LG
Item Response Theory (IRT) has recently been proposed as a framework for evaluating large language model (LLM) benchmarks by separating a model's latent ability from the properties of individual benchmark items. Existing neural IRT approaches, including PSN-IRT, estimate these quantities using point estimates, limiting uncertainty quantification and downstream statistical inference. We introduce Laplace-PSN-IRT, a post-hoc last-layer Laplace...
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