Introduction to Evaluating LLM-Powered Applications
Helping iterative projects move in the right direction is crucial for their success. Originally published on Towards AI, this article discusses the importance of evaluating the outputs of Large Language Models (LLMs) and how empirical statistical techniques can be used to enable confidence in any statement of improvement.
The Challenge of Evaluating LLMs
As applications powered by LLMs become more complicated, multi-stage, and empowered to take important decisions, evaluation of their outputs becomes increasingly important. Evaluation is challenging because of the non-deterministic nature of outputs from generative models, and the fact that it’s often difficult to even quantify the quality of an output with a numerical score.
The Importance of Metrics
Unlike more traditional ML, there are few data-related prerequisites to getting started with an LLM project, meaning that it’s possible to get quite far without even thinking about defining and computing metrics. Nevertheless, a metrics-based approach is important for meaningful iterative improvement and confidence in the results.
Empirical Statistical Techniques
In this article, we’ll use a simple example to show how it’s possible to use empirical statistical techniques — namely permutation and bootstrap testing — to evaluate the results of an LLM-powered application. There’s an interesting compromise between rigor and cost here, and each project’s needs will likely be different. The code associated with this article can be found here.
Example: Evaluating a Statistical Test
ChatGPT’s interpretation of “A quirky robot evaluates a statistical test” is shown below. This is not the first example of 3-armed robot images generated by AI. The image was generated by the author.
Conclusion
Evaluating the outputs of LLM-powered applications is crucial for their success. By using empirical statistical techniques such as permutation and bootstrap testing, it’s possible to enable confidence in any statement of improvement. Each project’s needs will likely be different, and there’s an interesting compromise between rigor and cost. By taking a metrics-based approach, developers can ensure meaningful iterative improvement and confidence in the results.
FAQs
- What is the main challenge of evaluating LLMs?
The main challenge of evaluating LLMs is the non-deterministic nature of outputs from generative models, and the fact that it’s often difficult to even quantify the quality of an output with a numerical score. - Why is a metrics-based approach important for LLM-powered applications?
A metrics-based approach is important for meaningful iterative improvement and confidence in the results. - What empirical statistical techniques can be used to evaluate LLM-powered applications?
Permutation and bootstrap testing are two empirical statistical techniques that can be used to evaluate LLM-powered applications. - Where can I find the code associated with this article?
The code associated with this article can be found here.
