- What are the best practices for testing the output quality of a Language Model like LLM, specifically to prevent hallucinations and ensure the accuracy of responses?
- How can I automate the testing process for a Language Model to ensure its performance under different loads and conditions?
- How can I ensure that the LLM is not susceptible to prompt injection attacks during testing? What are the best strategies to prevent this?
- What measures can I take during test automation to prevent data leakage in a Language Model implementation? Are there any specific tests that I should include in my test suite for this?
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I think you’ve done a good job identifying risks to focus on. It’s tempting to think of LLMs and AI as something new and spooky, but at least some of your questions seem to have reduced it to something more managable that you can probably apply existing testing techniques to, like load testing and security testing. Then you’ll probably want to do a lot of focused exploratory testing. Sorry I can’t be more helpful!
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I can only really answer point number 1. But first, I will say that the term “best practices” is often not very helpful–there are good practices in context, but not really universal “best” practices. I think it’s better to have heuristics and to adapt practices to a specific goal, need, etc.
Having said that, this list of LLM “syndromes” may be a useful set of heuristics for what you’re wanting to do:
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The only things I am aware of are the various tests used to compare LLMs to eachother for “leaderboards” it seems to be very subjective yet as far as the “quality of answers” there are a couple people out there who use a specific set of questions asked of LLMs to gauge the (subjective) quality of LLMs in comparison.
Since this is the wild west for this new tech, you will probably have to develop your own tactics. I think you are doing a good job of identifying the valuable information that it is desired to extract via QA activities. But I think the specifics of activities are up to you to roll your own.
Since many of the Open source LLMs and frameworks rely heavily on Python, you might consider Pytest + Python as a framework combination to start working with. You could probably use that to automate query and response for some evaluation.
Oh heck you might even train an LLM with the purpose of testing LLMs!
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As it so happens I just ran across this Intro to Testing Machine Learning Models
I dont know if it suits your needs but it looks like something useful might be there?
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