41 citations · 50 across the 8 of their papers we have counts for
6 papers · 1 filter
MedArena: Comparing LLMs for Medicine-in-the-Wild Clinician Preferences
Eric Wu, Kevin Wu, Jason Hom +11
Large language models (LLMs) are increasingly central to clinician workflows, spanning clinical decision support, medical education, and patient communication. However, current eva…
Disentangling Reasoning and Knowledge in Medical Large Language Models
Rahul Thapa, Qingyang Wu, Kevin Wu +11
Medical reasoning in large language models (LLMs) aims to emulate clinicians' diagnostic thinking, but current benchmarks such as MedQA-USMLE, MedMCQA, and PubMedQA often mix reaso…
MedCaseReasoning: Evaluating and learning diagnostic reasoning from clinical case reports
Kevin Wu, Eric Wu, Rahul Thapa +7
Doctors and patients alike increasingly use Large Language Models (LLMs) to diagnose clinical cases. However, unlike domains such as math or coding, where correctness can be object…
FineTuneBench: How well do commercial fine-tuning APIs infuse knowledge into LLMs?
Eric Wu, Kevin Wu, James Zou
There is great interest in fine-tuning frontier large language models (LLMs) to inject new information and update existing knowledge. While commercial LLM fine-tuning APIs from pro…
ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidence
Kevin Wu, Eric Wu, James Zou
Retrieval augmented generation (RAG) is frequently used to mitigate hallucinations and provide up-to-date knowledge for large language models (LLMs). However, given that document r…
How well do LLMs cite relevant medical references? An evaluation framework and analyses
Kevin Wu, Eric Wu, Ally Cassasola +7
Large language models (LLMs) are currently being used to answer medical questions across a variety of clinical domains. Recent top-performing commercial LLMs, in particular, are al…