Latest   Machine Learning Data-Driven LLM Evaluation with Statistical Testing 0 like April 16, 2025 Share this post 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.ChatGPT’s interpretation of “A quirky robot evaluates a statistical test”. This is not the first example I’ve seen of 3-armed robot images generated by AI … Image generated by the author. 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. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI Towards AI - Medium Share this post