Evaluation metrics for any kind of LLM app: RAG, chat, summarization, etc.

Dec 13, 2023 ·

Evaluation metrics for language and learning models (LLMs) are important to understand the performance of an LLM in a production environment. These metrics can help determine if an LLM is performing as expected, identify issues, and inform decisions about future development. The most commonly used evaluation metrics for LLMs include accuracy, precision, recall, F1 score, and training/inference time. Accuracy measures how accurately an LLM predicts the correct answer. Precision measures the ratio of true positives to all predicted positives. Recall measures the ratio of true positives to all actual positives. The F1 score is the harmonic mean of precision and recall, providing a more holistic measure of accuracy. Training/Inference Time is used to measure the speed at which an LLM can analyze data and deliver results.

Accuracy is the most widely used metric for evaluating LLMs, as it provides an overall measure of how well an LLM performs. It compares the LLM's predictions against a test set and calculates the percentage of correctly predicted results. However, accuracy alone does not provide a complete picture of an LLM's performance. In cases where two LLMs have similar accuracy, but one has higher precision or recall, the latter is likely to be a better choice.

Precision and recall focus on different aspects of an LLM's performance. Precision measures the fraction of positive predictions that are correct, while recall measures the fraction of relevant items that were retrieved. A high precision means that most of the LLM's predictions are correct, while a high recall means that most of the correct answers were found. The F1 score combines precision and recall into a single score, allowing users to compare the performance of multiple LLMs in one metric.

Training/Inference time measures how long it takes an LLM to process data and deliver results. This is important when using an LLM in real-time applications where quick responses are critical. Faster training/inference times can lead to improved user experience, while longer training/inference times may cause delays in response times.

Overall, these evaluation metrics are essential for understanding the performance of an LLM in a production environment. By measuring accuracy, precision, recall, F1 score, and training/inference time, developers can make informed decisions about their LLM's performance and take steps to improve it.