ABSOL: Aggregated Bayesian Subsampling Orchestrated with LLMs

• URL: http://arxiv.org/abs/2609.15007v1
• Date: Mon, 14 Sep 2026 04:15:05 GMT
• Title: ABSOL: Aggregated Bayesian Subsampling Orchestrated with LLMs
• Abstract summary: Large language models are increasingly used as natural-language interfaces to structured data.<n>We introduce ABSOL, a hybrid Bayesian network structure-learning framework that uses LLMs as bounded semantic guides.<n>We show that language-derived semantic knowledge can substantially improve scalable probabilistic structure learning.
• Score: 9.475016959358637
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models are increasingly used as natural-language interfaces to structured data, yet they remain unreliable when answers require consistent evidence conditioning, dependency-aware reasoning, and uncertainty estimation. Bayesian networks provide an explicit probabilistic reasoning layer, but learning useful structures from data remains costly and fragile at scale. We introduce ABSOL, a hybrid LLM-guided Bayesian network structure-learning framework that uses LLMs as bounded semantic guides. Across five discrete BN benchmarks spanning 27 to 1041 nodes, ABSOL is the only evaluated method to produce a viable graph on every benchmark, and achieves the highest Edge F_1 on every benchmark larger than 27 nodes with GPT-5.4. The four LLM augmentations, which contribute complementary semantic evidence to the statistical backbone, improve Edge F_1 over the non-LLM aggregation backbone by +0.23 on average. Complementary post-hoc refinement experiments suggest that these gains depend in part on limiting the LLM's authority over the final structure. Together, these results show that language-derived semantic knowledge can substantially improve scalable probabilistic structure learning when used as bounded guidance within a statistically grounded reasoning pipeline. The code for ABSOL is available at github.com/megagonlabs/absol-bn.

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