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The first thing that popped into my head is what do you do with the test results? Specifically, how do they feed back into model improvement in a way that avoid
by andy99 3y ago
The first thing that popped into my head is what do you do with the test results? Specifically, how do they feed back into model improvement in a way that avoids overfitting? Do you think having some kind of classical "holdout" question set is enough? Especially with RAG, I'd wonder with the levers that are available (prompt, chunking strategy, ...) if you define a bunch of test questions do you end up overfitting to them, or to the current data set. How can findings be extrapolated to new situations?
- maxrmk 3y agoAh! Think of this more like software testing that goes in CI/CD rather than an ML test or validation set. We're providing this testing for applications built on top of language models. For example if you're a SWE working on bing chat, you can make a change to how retrieval works and quickly know how it affected accuracy on a range of different test scenarios. This kind of evaluation is done by contractors today, and they are slow and inaccurate.