🌟 Hugging Face Guidebook on Evaluating Large Language Models
Hugging Face released a guidebook (https://github.com/huggingface/evaluation-guidebook) on Github for evaluating LLMs. It covers various evaluation methods, guides for developing custom evaluations, and practical tips.
The guide discusses different evaluation methods: automated tests, human evaluation, and other models. It also focuses on avoiding inference issues and ensuring consistent results.
Key sections:
🟢 Automated benchmarks
🟢 Human evaluation
🟢 LLM as a judge
🟢 Troubleshooting
🟢 Basic knowledge
Start with the Basics section for an introduction to evaluation and benchmarks. The basic knowledge (https://github.com/huggingface/evaluation-guidebook?tab=readme-ov-file#general-knowledge) section explains important LLM topics, such as inference and tokenization.
Practical sections:
🔹 Tips and recommendations (https://github.com/huggingface/evaluation-guidebook/blob/main/contents/Model%20as%20a%20judge/Tips%20and%20tricks.md)
🔹 Troubleshooting (https://github.com/huggingface/evaluation-guidebook?tab=readme-ov-file#troubleshooting)
🔹 Designing evaluation prompts (https://github.com/huggingface/evaluation-guidebook/blob/main/contents/Model%20as%20a%20judge/Designing%20your%20evaluation%20prompt.md)
Future guide plans:
🟠 Describing automated metrics
🟠 Key points to consider when building tasks
🟠 Why LLM evaluation is necessary
🟠 Challenges of comparing models
🖥 Github (https://github.com/huggingface/evaluation-guidebook)
