I implemented 12+ LLM evaluation metrics so you don't have to

Dec 13, 2023 ·

In the article titled "d I implemented 10+ LLM evaluation metrics, so you don't have to" by /u/ArtemAbrams, the author discusses the importance of evaluating Language and Machine Learning Models (LLM). They begin by discussing the need for an objective measure that accurately quantifies the performance of a model and how difficult this is to do. The author then goes on to present 10+ evaluation metrics, with detailed explanations of each one. These metrics include precision, recall, f1 score, ROC AUC, accuracy, log-loss, BLEU score, perplexity, inverse document frequency, and more.

The article also explains how these metrics can be used together to evaluate both supervised and unsupervised models. Furthermore, it provides examples of different use cases and when to use each metric. Finally, it offers useful tips on how to avoid common pitfalls and mistakes when using these metrics.

Overall, the article provides a comprehensive overview of how to properly and effectively evaluate LLM models. It gives readers a good understanding of the various metrics available and provides clear guidelines on when and how to use them. This is an invaluable resource for those looking to develop more accurate, robust models.