Overview of our two-stage fine-tuning strategy. We run prompt-tuning at

Context-Aware Robust Fine-Tuning

MetaICL Learning to Learn In Context (NAACL 2022)_哔哩哔哩_bilibili

Knowledge Graphs & LLMs: Fine-Tuning vs. Retrieval-Augmented Generation - Graph Database & Analytics

Instruction Fine-Tuning: Does Prompt Loss Matter?

Patterns for Building LLM-based Systems & Products

Full Fine-Tuning, PEFT, Prompt Engineering, or RAG?

RLHF & DPO: Simplifying and Enhancing Fine-Tuning for Language Models

Cho-Jui HSIEH, University of Texas at Austin, TX, UT, Department of Computer Science

Fine-Tuning Tutorial: Falcon-7b LLM To A General Purpose Chatbot

Prompting: The new era of Natural Language Processing