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Bayesian subsampling

topic1 events
papersTODAY 04:00 UTC

ABSOL Framework Combines Bayesian Subsampling with LLMs for Structured Data

A new arXiv paper introduces ABSOL, a method that pairs aggregated Bayesian subsampling with large language models to improve reasoning over structured data. The approach targets cases where reliable answers depend on consistent evidence, dependency-aware reasoning, and estimated uncertainty. The authors frame the work as addressing the unreliability of LLMs used as natural-language interfaces to Bayesian networks.

bayesian/llm/uncertainty/research