LLMOps

Chapter 7. Evaluation for LLMs

Language models have become increasingly sophisticated, but assessing their effectiveness accurately remains a significant challenge.

The importance of LLM evaluation has garnered attention not only from academia but also from industry stakeholders. This convergence of research and testing efforts signifies the importance of the problem and the collective determination to find effective solutions. It also accelerates the pace of innovation, helping researchers understand and improve these models further.

In academia, researchers have been exploring new methodologies, developing innovative metrics, and conducting rigorous experiments to push the boundaries of LLM evaluation Although there are some leading contenders, there are no clear winners yet, since many metrics and scoreboards end up being useful for just a short period or for a narrow set of applications. Regardless, industry players are keenly aware of the practical implications of LLM performance.

At its core, evaluation aims to gauge how well an LLM accomplishes its intended purpose, whether it’s generating coherent and contextually relevant text, understanding user input, or completing specific tasks. In this chapter, you’ll learn about a systematic framework designed to tackle this challenge for different applications, along with some tips on what has worked.

Why Evaluation Is a Hard Problem

Evaluating LLMs is the process of assessing their performance and capabilities. It involves a ...