Pre-training vs Fine-Tuning vs In-Context Learning of Large

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.

Everything You Need To Know About Fine Tuning of LLMs

Symbol tuning improves in-context learning in language models – Google Research Blog

In-Context Learning Approaches in Large Language Models, by Javaid Nabi

Adaptation

Pre-trained Models for Representation Learning

Empowering Language Models: Pre-training, Fine-Tuning, and In-Context Learning, by Bijit Ghosh

Fine-Tuning LLMs: In-Depth Analysis with LLAMA-2

In-Context Learning, In Context

Illustrating Reinforcement Learning from Human Feedback (RLHF)

Fine-Tuning Insights: Lessons from Experimenting with RedPajama

How to Fine-tune Llama 2 with LoRA for Question Answering: A Guide

How to Use Hugging Face AutoTrain to Fine-tune LLMs - KDnuggets

Flat Young Man Repair Finetune Gears Stock Vector (Royalty Free) 1327703738

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