3 papers
cs.CL2025
Counting Clues: A Lightweight Probabilistic Baseline Can Match an LLM
Furong Jia, Yuan Pu, Finn Guo +1
Large language models (LLMs) excel on multiple-choice clinical diagnosis benchmarks, yet it is unclear how much of this performance reflects underlying probabilistic reasoning. We…
cs.LG2025
Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025
Emily Alsentzer, Marie-Laure Charpignon, Bill Chen +90
The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025…
cs.CL2025
Diagnosing our datasets: How does my language model learn clinical information?
Furong Jia, David Sontag, Monica Agrawal
Large language models (LLMs) have performed well across various clinical natural language processing tasks, despite not being directly trained on electronic health record (EHR) dat…