How to evaluate fine-tuned model performance
So far, we’ve learned how to fine-tune LLMs to suit our needs, but how do we evaluate a model to make sure it’s performing well? But how do we know if a fine-tuned model made improvements over its predecessor model over a particular task? What are some industry-standard benchmarks that we can rely on to evaluate the models? In this section, we will see how LLMs such are GPT are evaluated and use the most popular benchmarks developed by researchers.
Evaluation metrics
Bilingual Evaluation Understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) are both widely used metrics for evaluating the quality of machine-generated text, especially in the context of machine translation and text summarization. They measure the quality of generated texts in different ways. Let’s take a closer look.
ROUGE
ROUGE is a set of metrics that’s used to evaluate the quality of summaries by comparing...
