Maximizing LLM & Image Model Performance: Advanced Evaluation Strategies

Generative AI has revolutionized various industries by enabling the creation of realistic and customized content. However, evaluating the performance and quality of generative AI models can be challenging. In this course, you’ll explore different evaluation techniques and their trade-offs to help you build effective customer valuation metrics for generative AI models.

The first evaluation technique we will discuss is programmatic rule-based evaluation. This approach involves creating custom rules to determine the quality of the generated output. For example, in the context of generating email content, you can evaluate the length of the email or check if it contains specific keywords. The advantage of this technique is its quick feedback loop and cost-effectiveness. However, it may not be suitable for tasks that require nuanced evaluation.

The second technique is LLM (Language Model)-based evaluation. Here, instead of relying on custom rules, another LLM is used to evaluate the generated output. This approach is useful for tasks that are too nuanced for rule-based evaluation, such as evaluating the helpfulness or readability of text. Multiple outputs are generated by the LLM, and another LLM is used to rate each output. While this technique provides more accurate evaluation, it is slower and more costly due to the need for human intervention.

The third technique is human-based evaluation, where human evaluators rate the generated output. This approach is ideal for high-stakes tasks where false positives or false negatives can have significant consequences. However, human-based evaluation is time-consuming, costly, and requires a labeling step. It provides the highest level of accuracy but may not be feasible for large-scale evaluations.

To illustrate these evaluation techniques, we provide code examples using different scenarios. In the programmatic rule-based evaluation example, we demonstrate how to set up evaluation metrics using Python libraries like pandas, numpy, and TQDM. We define custom rules, such as the minimum length of a social media blog post or the presence of specific keywords, and evaluate the generated output accordingly.

In the LLM-based evaluation example, we show how to use one LLM to generate multiple outputs and another LLM to rate each output. We discuss the importance of ground truth labels and how synthetic data generated by a powerful model can be used for evaluation.

In the human-based evaluation example, we generate images using OpenAI's GPT-3.5-Turbo and ask human evaluators to rate the quality of the images. We demonstrate an interactive approval system that allows evaluators to provide feedback on each output.