Instruction-Following Evaluation for Large Language Models
arxiv.org
Instruction-Following Evaluation for Large Language Models
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
"My benchmark for large language models"
https://t.co/YZBuwpL0tl
Nice post but even more than the 100 tests specifically, the Github code looks excellent - full-featured test evaluation framework, easy to extend with further tests and run against many... See more
🔥 YC outlines how top AI startups prompt LLMs: prompts exceeding six pages, XML tags, meta-prompts and evaluations as their core IP.
They found meta-prompting and role assignment drive consistent, agent-like behavior.
⚙️ Key Learning
→ Top AI startups use... See more