Nice paper for a long read across 114 pages. "Ultimate Guide to Fine-Tuning LLMs" Some of the things they cover 📊 Fine-tuning Pipeline Outlines a seven-stage process for fine-tuning LLMs, from data preparation to deployment and... See more

New Anthropic research: We elicit capabilities from pretrained models using no external supervision, often competitive or better than using human supervision. Using this approach, we are able to train a Claude 3.5-based assistant that beats its human-supervised counterpart. https://t.co/p0wKBtRo7q

How do models represent style, and how can we more precisely extract and steer it?

A commonly requested feature in almost any LLM-based writing application is “I want the AI to respond in my style of writing,” or “I want the AI to adhere to this style guide.” Aside from costly and complicated multi-stage finetuning processes like Anthropic’s RL with... See more