10 items tagged with "Bayesian"
ACL 2026 presentation on ranking reasoning LLMs under test-time scaling: dense repeated-trial evaluation, Bayes@N as a practical default, low-budget priors, categorical ranking, and the Scorio toolkit.
Serving reasoning LLMs efficiently and reliably: lossless DFloat11 compression, KV-cache quantization, and Bayes@N evaluation and ranking under test-time scaling.
Serving reasoning LLMs efficiently and reliably: lossless DFloat11 compression, KV-cache quantization, and Bayes@N evaluation and ranking under test-time scaling.
Ranking reasoning LLMs under test-time scaling. We compare 72 ranking methods (Bayes@N, Bradley-Terry, Elo, IRT, voting, graph/spectral) across 20 models and four Olympiad-style math benchmarks. At full budget they mostly agree (Kendall's tau_b 0.93-0.95); at one trial a greedy prior cuts variance 16-52% but can bias the ranking. Packaged in the Scorio toolkit.
Ranking reasoning LLMs under repeated sampling, comparing 72 ranking methods across four Olympiad-style math benchmarks and packaging them in Scorio.
ICLR 2026 presentation on Don't Pass@k: a Bayesian evaluation framework (Bayes@N) with Dirichlet posteriors, credible intervals, a non-overlap decision rule, categorical rubric scoring, and the Scorio toolkit.
Proposed a Bayesian framework that estimates models' success probabilities with quantified uncertainty, yielding more reliable rankings and enabling categorical evaluation of LLMs.
Day 3 of the SCIPE workshop on large language models: attention under the hood and open research directions — why Pass@k is unstable, Bayes@N posteriors, rankings with uncertainty, and ranking reasoning LLMs under test-time scaling.
M.Sc. thesis defense on evaluating and ranking reasoning LLMs under a fixed test-time sampling budget, covering Pass@k's instability, Bayesian alternatives, and how many samples a reliable ranking actually needs.
A Bayesian framework for evaluating large language models that replaces unstable Pass@k metrics with posterior estimates and credible intervals. The method improves sample efficiency, supports graded outcomes, and enables statistically sound model comparisons.