12 Evaluations and benchmarks
This chapter covers
- Understanding the significance of benchmarking and evaluating LLMs
- Learning different evaluation metrics
- Benchmarking model performance
- Implementing comprehensive evaluation strategies
- Best practices for evaluation benchmarks and key evaluation criteria to consider
Taking into account the recent surge of interest in GenAI and specifically in large language models (LLMs), it’s crucial to approach these novel and uncertain features cautiously and responsibly. Many leaderboards and studies 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 involves evaluating performance, comparing models, guiding improvements, accelerating technological advancement, managing costs and latency, and ensuring 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.