Model evaluation

In model evaluation, the objective is to assess the capabilities of a single model without any prompt engineering, RAG pipeline, and so on.

This evaluation is essential for several reasons, such as selecting the most relevant LLM or making sure that the fine-tuning process actually improved the model. In this section, we will compare ML and LLM evaluation to understand the main differences between these two fields. We will then explore benchmarks for general-purpose, domain-specific, and task-specific models.

Comparing ML and LLM evaluation

ML evaluation is centered on assessing the performance of models designed for tasks like prediction, classification, and regression. Unlike the evaluation of LLMs, which often focuses on how well a model understands and generates language, ML evaluation is more concerned with how accurately and efficiently a model can process structured data to produce specific outcomes.

This difference comes from the nature of...