Evaluating LLM performance metrics offline
In the development of LLMs, a key step is the evaluation of performance through offline metrics. This step allows developers to assess how well the model is likely to perform in real-world scenarios, based on data from past interactions rather than live input. This offline analysis helps to identify areas for improvement in accuracy, response quality, and overall reliability.
Evaluating binary, multi-class, and multi-label metrics
Accuracy is a fundamental metric used to determine the percentage of a model’s predictions that are correct. For example, we can evaluate the accuracy of an LLM by comparing its binary yes or no responses against a set of pre-labeled data that serves as the ground truth. By tallying the instances where the LLM’s output aligns with the human-provided labels, we can quantify its accuracy. However, accuracy alone can be misleading, especially in unbalanced datasets where some classes are overrepresented...
