Best Practices for Model Evaluation

Proper evaluation helps ensure that models meet desired standards of accuracy, fairness, and robustness, while also guiding improvements and minimizing biases. In this chapter, we will explore best practices for evaluating LLMs, providing a comprehensive approach to ensure that model assessments are rigorous, consistent, and actionable.

Define Clear Evaluation Objectives

Before you start evaluating a model, it’s important to know what you want to measure. Here are some things to think about:

• Task relevance: Ensure your evaluation matches the model's intended tasks, like conversation, problem-solving, or summarization.

• Choose the right metrics: Pick metrics that are relevant to what you’re measuring. For example, if the model is summarizing text, you might want to measure ROUGE scores. If it's generating text, you could use BLEU or perplexity.

• Real-world use: Consider how well the model would perform in a real-world scenario, not just in controlled tests.

Use a Range of Evaluation Methods

It’s important to use a range of evaluation methods when assessing your model, as relying on just one approach can provide an incomplete picture of its performance. Assess accuracy to measure correct outputs, generalization to see how it handles unseen data, and fairness to check for biases. Evaluate robustness by testing how it handles tricky inputs and ensure explainability, especially in sensitive tasks, to confirm the model can justify its responses.

Evaluate the Model Throughout Development

Evaluation shouldn’t just happen at the end of the process. Regularly check the model’s performance at different stages:

• Before training: Test how well the model understands basic language patterns before it starts training on specific tasks.

• During training: Regularly check how the model is improving by testing it on validation sets.

• After training: Once training is done, perform a thorough test on fresh data to see how well the model has learned and generalizes.

Monitor for Model Drift

Over time, models can lose their effectiveness as data changes, so it’s important to monitor their performance regularly, especially after deployment in real-world applications. Periodically retraining the model with new data helps maintain its accuracy and relevance. Using version control allows you to track changes and improvements to the model, ensuring you have a clear record of its evolution.