Pre-training vs Fine-Tuning vs In-Context Learning of Large
By A Mystery Man Writer
Description
Large language models are first trained on massive text datasets in a process known as pre-training: gaining a solid grasp of grammar, facts, and reasoning. Next comes fine-tuning to specialize in particular tasks or domains. And let's not forget the one that makes prompt engineering possible: in-context learning, allowing models to adapt their responses on-the-fly based on the specific queries or prompts they are given.
Domain Specific Generative AI: Pre-Training, Fine-Tuning, and RAG — Elastic Search Labs
Prompting: Better Ways of Using Language Models for NLP Tasks
Pretraining vs Fine-tuning vs In-context Learning of LLM (GPT-x
Can prompt engineering methods surpass fine-tuning performance with pre-trained large language models?, by lucalila
A High-level Overview of Large Language Models - Borealis AI
Symbol tuning improves in-context learning in language models – Google Research Blog
Fine-tuning vs Context-Injection (RAG) - Prompting - OpenAI Developer Forum
Fine Tuning vs. Prompt Engineering Large Language Models •
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