Automating Behavioral Evaluations for LLMs: A Practical Guide to Bloom

If you've ever deployed a large language model (LLM) in production, you might know the uncertainty that comes with it. Will the model refuse a legitimate request? Will it be too agreeable when it shouldn't be? How does one even test for behaviors that emerge only in specific, hard-to-predict scenarios? Manual red-teaming and hand-crafted evaluation suites have been the standard approach, but they can be very hard to scale. They're expensive, time-consuming, and worst of all, they become obsolete the moment they're published, since models can be trained on them.