Data-Driven LLM Evaluation with Statistical Testing
Last Updated on April 16, 2025 by Editorial Team
Author(s): Robert Martin-Short
Originally published on Towards AI.
Helping iterative projects move in the right direction.
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 and enable confidence in any statement of improvement that’s made. There’s an interesting compromise between rigor and cost here, and each project’s needs will likely be different. Please see here for the code associated with this article.
As applications powered by Large Language Models (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. 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… Read the full blog for free on Medium.
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