A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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Parameter-efficient fine-tuning (PEFT) achieves better performance and lower cost than in-context learning for few-shot tasks.
Simone Kresevic, Mauro Giuffrè, Miloš Ajčević, et al.
A RAG-based GPT-4 Turbo framework with structured guideline reformatting and prompt engineering improves clinical decision support accuracy from 43% to 99% for chronic Hepatitis C management.
Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.
This paper systematically surveys prompt-based learning in NLP, a paradigm where pre-trained language models are adapted to tasks via textual prompts for few-shot or zero-shot learning.
Tom B. Brown, Benjamin Mann, Nick Ryder, et al.
Showed that scaling language models to 175 billion parameters (GPT-3) enables in-context learning — performing new tasks from just a few examples in the prompt, without fine-tuning.