2311.16452.pdf

N/A

Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine Harsha Nori*‡ , Yin Tat Lee* , Sheng Zhang* , Dean Carignan, Richard Edgar, Nicolo Fusi, Nicholas King, Jonathan Larson, Yuanzhi Li, Weishung Liu, Renqian Luo, Scott Mayer McKinney† , Robert Osazuwa Ness, arXiv:2311.16452v1 [cs.CL] 28 Nov 2023 Hoifung Poon, Tao Qin, Naoto…

related reading

• gpt-4.pdfcdn.openai.com
• The Power of Prompting - Microsoft Researchmicrosoft.com
• Large language models are few-shot clinical information extractorsarxiv.org
• 2309.07430.pdfarxiv.org
• [2201.11903] Chain of Thought Prompting Elicits Reasoning in Large Language Modelsarxiv.org
• Do we still need clinical language models?proceedings.mlr.press
• 2403.09611.pdfarxiv.org
• [2005.14165] Language Models are Few-Shot Learnersarxiv.org
• [2507.19457] GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learningarxiv.org
• [2206.02696] Learning to Ask Like a Physicianarxiv.org
• Language Models can Solve Computer Tasksarxiv.org
• gpt-4-system-card.pdfcdn.openai.com