LLM Evaluation Guidebook:

1. Automatic Benchmarks:

• Automated evaluations utilize predefined datasets and metrics to assess LLM performance without human intervention.

2. Human Evaluation:

• Basics: Involves human reviewers assessing LLM outputs based on criteria like relevance, coherence, and accuracy.
• Using Human Annotators: Guidelines on selecting and training annotators to ensure consistent and unbiased evaluations.
• Tips and Tricks: Recommendations include clear annotation guidelines, pilot testing, and regular calibration sessions among annotators.

3. LLM-as-a-Judge:

• Basics: Leveraging LLMs to evaluate outputs from other models or systems.
• Getting a Judge-LLM: Steps to select or fine-tune an LLM specifically for evaluative tasks.
• Designing Your Evaluation Prompt: Crafting prompts that elicit accurate and consistent evaluations from the judge-LLM.
• Evaluating Your Evaluator: Methods to assess the reliability and validity of the judge-LLM’s evaluations.
• What About Reward Models: Discussion on using reward models that predict scores based on human preferences to guide LLM evaluations.
• Tips and Tricks: Addressing biases, ensuring consistency, and implementing self-consistency checks in LLM-based evaluations.