A multi-agent hierarchical Bayesian model corrects selection bias in LLM user feedback via topic clustering and reweighting with mild priors on the feedback channel to recover accurate aggregate quality estimates.
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
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CommitDistill is a deterministic, local-only prototype that extracts typed knowledge from git commits and evaluates retrieval performance against baselines on public repositories.
citing papers explorer
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Correcting Selection Bias in Sparse User Feedback for Large Language Model Quality Estimation: A Multi-Agent Hierarchical Bayesian Approach
A multi-agent hierarchical Bayesian model corrects selection bias in LLM user feedback via topic clustering and reweighting with mild priors on the feedback channel to recover accurate aggregate quality estimates.
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CommitDistill: A Lightweight Knowledge-Centric Memory Layer for Software Repositories
CommitDistill is a deterministic, local-only prototype that extracts typed knowledge from git commits and evaluates retrieval performance against baselines on public repositories.