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🌟 A guidebook for evaluating large language models from Hugging Face
Hugging Face has released a guide on GitHub for evaluating LLMs (Large Language Models).
It compiles various methods for evaluating models, guidelines for developing your own evaluations, as well as tips and recommendations from practical experience. The guide discusses different ways of evaluation: using automated tests, humans, or other models.
Special attention is given to how to avoid issues with model inference and make the results consistent. The guide offers advice on how to clean the data, how to use prompts for communicating with LLMs, and how to analyze unexpected poor results.
If you’re new to evaluation and benchmarking, you should start with the Basics sections in each chapter before diving deeper. In the General Knowledge section, you’ll also find explanations that will help you understand important LLM topics, such as how model inference works and what tokenization is.
More practical sections include: Tips and Tricks, Troubleshooting, and sections dedicated to Designing your Evaluation Prompt.
▶️Table of Contents:
🟢Automated Benchmarks
🟢Human Evaluation
🟢LLM as a Judge
🟢Troubleshooting
🟢General Knowledge
📌 Future Guide Plans:
🟠Description of automated metrics;
🟠Key points to always consider when building a task;
🟠Why LLM evaluation is needed;
🟠Why comparing models is difficult.