The paper investigates how Large Language Models can be used to approximate domain expert priors for Bayesian Networks by extracting probabilistic knowledge about real‑world events. Experiments on eighty publicly available networks across domains such as healthcare and finance show that LLM‑derived conditional probabilities outperform random, uniform, and next‑token baselines. The authors also demonstrate that these LLM‑generated priors can refine data‑driven distributions, especially when data is scarce, and provide the first comprehensive baseline for evaluating LLM performance in probabilistic knowledge extraction.
arXiv:2601.17609v3 Announce Type: replace Abstract: In domains like medicine and finance, large-scale labeled data is costly and often unavailable, leading to models trained on small datasets that st...
arXiv:2609.15007v1 Announce Type: cross Abstract: Large language models are increasingly used as natural-language interfaces to structured data, yet they remain unreliable when answers require consis...
arXiv:2607. 14141v1 Announce Type: new Abstract: Bayesian Belief Networks (BBNs) are powerful tools for decision-making under uncertainty.
arXiv:2606. 19264v1 Announce Type: new Abstract: The knowledge encoded in large language models (LLMs) can serve as a substrate for structured reasoning over variables describing a complex world, but accessing this knowledge in a probabilistically coherent manner poses a difficult inference problem.
The paper investigates whether large language models (LLMs) can perform principled Bayesian inference when updating beliefs in context. By creating an evaluation framework that includes qualitative epistemological tests, quantitative benchmarks, and analyses of downstream impacts, the authors find that LLMs typically violate Bayes' rule but that their performance improves dramatically when equipped with advanced reasoning and code‑execution tools, allowing them to approximate analytically intractable posteriors. The study suggests that appropriately tool‑augmented LLMs could already be leveraged to accelerate scientific discovery through complex belief updates.
Semantic Bayesian World Models (SBWMs) propose a shift from static knowledge graphs to a dynamic, probabilistic fabric of beliefs that can be updated via Bayesian conditioning and influenced by actions. The approach aims to bridge the gap between crisp factual assertions and the probabilistic reasoning of foundation models and autonomous agents, enabling richer inference in scenarios such as home‑security decisions, actuarial estimates, and planning tasks. Realizing SBWMs requires new tools for belief annotation, probabilistic entailment, semantic calibration, and protocols for belief exchange among agents.
arXiv:2609.15992v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have rendered them necessary for Natural Language Processing (NLP) tasks, and their high inference cost...
arXiv:2609.21673v1 Announce Type: new Abstract: Probabilistic Graphical Models (PGMs), especially Bayesian Networks (BNs), expose directed structure and probabilistic parameters, making them natural...
arXiv:2606. 09856v1 Announce Type: cross Abstract: Post-training Large Language Models (LLMs) for reasoning typically focuses on deductive tasks such as mathematics and coding where correctness is verifiable.
arXiv:2604.03925v2 Announce Type: replace Abstract: Large language models (LLMs) struggle to accumulate evidence across multiple rounds of user interactions, failing to update their beliefs in a mann...
arXiv:2610.17016v1 Announce Type: cross Abstract: Evaluating model uncertainty on real-world data is challenging because each instance provides only a single observed label, leaving true conditional...
arXiv:2607. 22961v1 Announce Type: new Abstract: Verbalized Machine Learning (VML) parameterizes a model as a natural-language prompt that an LLM evaluates as f(x; theta).