arXiv:2607. 14141v1 Announce Type: new Abstract: Bayesian Belief Networks (BBNs) are powerful tools for decision-making under uncertainty.
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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:2602. 21889v2 Announce Type: replace-cross Abstract: Predictions from ML models support human decision making in several fields, including high-stakes ones such as healthcare and the judiciary.
ConsultMind is an uncertainty‑aware framework that automates diagnostic consultation by updating disorder posteriors after each patient response and using posterior uncertainty to guide inquiry and diagnosis. It builds on AutoDisym, a pipeline that constructs a Disorder–Symptom Bayesian Network (DSBN) from diagnostic knowledge and clinical narratives. Across psychiatry, respiratory medicine, fever clinics, and public datasets, AutoDisym produces high‑quality DSBNs and ConsultMind improves diagnostic accuracy and explanation quality, achieving up to 22.15‑point gains in Top‑1 accuracy and 37.89‑point gains in Top‑3 accuracy.
arXiv:2609.01188v1 Announce Type: new Abstract: Large Language Models (LLMs) are revolutionizing digital communication by powering conversational agents deployed across domains such as customer servi...
arXiv:2412.15957v2 Announce Type: replace-cross Abstract: The rapid development of large language models (LLMs) has transformed many industries, including healthcare. In practice, hospitals and patie...
arXiv:2605. 00696v2 Announce Type: replace-cross Abstract: We study adaptive querying for learning user-dependent quantities of interest, such as responses to held-out items and psychometric indicators, within tight query budgets.