5 papers
Batch-of-Thought: Cross-Instance Learning for Enhanced LLM Reasoning
Xuan Yang, Furong Jia, Roy Xie +4
Current Large Language Model reasoning systems process queries independently, discarding valuable cross-instance signals such as shared reasoning patterns and consistency constrain…
What Patients Really Ask: Exploring the Effect of False Assumptions in Patient Information Seeking
Raymond Xiong, Furong Jia, Lionel Wong +1
Patients are increasingly using large language models (LLMs) to seek answers to their healthcare-related questions. However, benchmarking efforts in LLMs for question answering oft…
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…
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…
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…