LLM Evaluation: Tutorial & Best Practices

Large Language Models (LLMs) significantly enhance productivity across various applications but pose challenges due to their nondeterministic nature and potential for errors and hallucinations. Evaluating LLMs is crucial to ensure their reliability, especially in critical applications, and involves assessing both the models and the systems they are part of. This process is complex due to the stochastic nature of text generation, requiring advanced evaluation metrics beyond simple benchmarks, which can be easily manipulated and do not capture the full range of an LLM's capabilities. Evaluation can be divided into model evaluation, which assesses a model's generic performance, and system evaluation, which focuses on a model's effectiveness in specific use cases. Various benchmarks, such as MMLU and GSM8K, are employed, despite their limitations like data leakage and cultural bias. Evaluation metrics include surface-form and semantic measures, with modern methods incorporating LLMs themselves as judges, hybrid evaluations combining human oversight, and robustness testing like red teaming. The article also discusses practical evaluation examples using tools like LaunchDarkly's AI Configs, emphasizing the need for ongoing model assessments to ensure performance aligns with real-world applications.