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.
Density-based cluster- ing based on hierarchical density estimates
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
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.