12 Evaluations and benchmarks
This chapter covers
- Understanding the significance of benchmarking and evaluating LLMs
- Learning different evaluation metrics across both traditional and newer GenAI-specific metrics
- Benchmarking model performance utilizing comprehensive benchmarks such as HELM, HEIM, MMLU, and HellaSWAG, to name a few
- Implementing comprehensive evaluation strategies, ensuring continuous improvement based on evaluative insights
- Best practices for Evaluation benchmarks and key evaluation criteria to consider
Given the recent surge of interest in GenAI and particularly LLMs, it's crucial to approach these novel and uncertain features with caution and responsibility. Many leaderboards and research have shown that LLMs can match human performance in various tasks, such as taking standardized tests or creating art, sparking enthusiasm and attention. However, their novelty and uncertainties necessitate careful handling.
The role of benchmarking LLMs in production deployment cannot be overstated. It evaluates performance, compares models, guides improvements, accelerates technological advancement, manages costs and latency, and ensures efficient task flow for real-world applications. While evaluations are part of LLMOps, their criticality in ensuring LLMs meet the demands of various applications warrants a separate discussion in this chapter.