5 citations · 9 across the 4 of their papers we have counts for
5 papers
SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning
Kaiwen Zhou, Ahmed Elgohary, A S M Iftekhar +1
The ability of LLM agents to plan and invoke tools exposes them to new safety risks, making a comprehensive red-teaming system crucial for discovering vulnerabilities and ensuring…
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…
AutoRedTeamer: Autonomous Red Teaming with Lifelong Attack Integration
Andy Zhou, Kevin Wu, Francesco Pinto +7
As large language models (LLMs) become increasingly capable, security and safety evaluation are crucial. While current red teaming approaches have made strides in assessing LLM vul…
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…