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.
By Aliakbar Nafar, Kristen Brent Venable, Zijun Cui, Parisa Kordjamshidi
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.
By Otto Nyberg, Fausto Carcassi, Davide Tugnoli, Giovanni Cin\`a
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.
By Xiao Sun, Yuming Yang, Yun Chen, Jiang Zhong, Junnan Zhu, Xinyi Jiang, Haoyang Zeng, Ruirui Chen, Yining Wang, Xinyu Zhou, Rong Tang, Kaiwen Wei
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...
By Rohan Kirti, Akash Ghosh, Aryan Vats, Niladri Ghosh, Shipra Shriparn, Roshni Ramnani, Anutosh Maitra, Sriparna Saha
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...
By Ruize Shi, Hong Huang, Wei Zhou, Kehan Yin, Kai Zhao, Yun Zhao
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.
By Kaizheng Wang, Yuhang Wu, Assaf Zeevi
In serious illness, human surrogates often struggle to accurately predict patient preferences (68% accuracy), causing decision conflict. Personalized Patient Preference Predictor (P4) agents offer a p...
The paper introduces P4-DT, a personalized patient preference predictor that uses dilemma training to elicit context‑dependent decision reasoning. In a study of 12 patient‑surrogate pairs, P4‑DT achieved 81.7% accuracy in predicting patient treatment choices, outperforming unassisted surrogates (55.0%) and surrogates aided by a simpler P4 model (61.7%). The authors show that incorporating contextual scenarios and open‑ended text into prompts improves accuracy by 15 percentage points over static value ratings.
By Natasha Ureyang, Sebastian Porsdam Mann, Yuxin Liu, Zuriel Hassirim, Melanie Almonte, Wenhao Chen, Joyce Ng, Thant Nay Lin, Aung Thiha, Gerald CH Koh, Brian David Earp, Pin Sym Foong
arXiv:2607. 03425v1 Announce Type: new Abstract: Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions, in potentially high-stakes scenarios.
By Denise Tampieri, Giovanni De Toni, Paolo Giudici
arXiv:2608.29453v1 Announce Type: cross
Abstract: As AI becomes increasingly integrated into clinical practice, it is playing a growing role in medical decision making. Medicine, however, is a high s...
By Jiayuan Zhu, Jiazhen Pan, Fenglin Liu, Minhao Hu, Junde Wu
arXiv:2508. 07617v2 Announce Type: replace-cross Abstract: AI has the potential to augment human decision making.
By Sarah Jabbour, David Fouhey, Nikola Banovic, Stephanie D. Shepard, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens
Researchers must often choose between Bayesian or neural network models of behavior, two paradigms with complementary strengths and weaknesses. An ideal paradigm would facilitate testing many kinds of...