174 citations · 208 across the 11 of their papers we have counts for
4 papers · 1 filter
From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond
Harsha Nori, Naoto Usuyama, Nicholas King +4
Run-time steering strategies like Medprompt are valuable for guiding large language models (LLMs) to top performance on challenging tasks. Medprompt demonstrates that a general LLM…
Steering Language Model Refusal with Sparse Autoencoders
Kyle O'Brien, David Majercak, Xavier Fernandes +7
Responsible deployment of language models requires mechanisms for refusing unsafe prompts while preserving model performance. While most approaches modify model weights through add…
MedFuzz: Exploring the Robustness of Large Language Models in Medical Question Answering
Robert Osazuwa Ness, Katie Matton, Hayden Helm +4
Large language models (LLM) have achieved impressive performance on medical question-answering benchmarks. However, high benchmark accuracy does not imply that the performance gene…
AI-Powered Reminders for Collaborative Tasks: Experiences and Futures
Katelyn Morrison, Shamsi Iqbal, Eric Horvitz
Email continues to serve as a central medium for managing collaborations. While unstructured email messaging is lightweight and conducive to coordination, it is easy to overlook co…