1.6k citations · 2.3k across the 7 of their papers we have counts for
6 papers · 1 filter
Sequential Diagnosis with Language Models
Harsha Nori, Mayank Daswani, Christopher Kelly +12
Artificial intelligence holds great promise for expanding access to expert medical knowledge and reasoning. However, most evaluations of language models rely on static vignettes an…
JSONSchemaBench: A Rigorous Benchmark of Structured Outputs for Language Models
Saibo Geng, Hudson Cooper, Michał Moskal +6
Reliably generating structured outputs has become a critical capability for modern language model (LM) applications. Constrained decoding has emerged as the dominant technology acr…
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…
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
Harsha Nori, Yin Tat Lee, Sheng Zhang +15
Generalist foundation models such as GPT-4 have displayed surprising capabilities in a wide variety of domains and tasks. Yet, there is a prevalent assumption that they cannot matc…
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan +11
Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks,…
Capabilities of GPT-4 on Medical Challenge Problems
Harsha Nori, Nicholas King, Scott Mayer McKinney +2
Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation across various domains, including medicine. We present a com…