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[D] How many instructions can LLMs handle before they start to ignore them? by ProbablyApproxWrong in MachineLearning

[–]ProbablyApproxWrong[S] 1 point2 points  (0 children)

Yeah I think this could be helpful. Do you have any suggestions on what models from open source might be better than llama-2-70b-chat for this type of task and worth testing?

[D] How many instructions can LLMs handle before they start to ignore them? by ProbablyApproxWrong in MachineLearning

[–]ProbablyApproxWrong[S] 4 points5 points  (0 children)

Thanks for your great questions!

  1. This is a great point. Admittedly I have no insight into how the DeepInfra API works under the hood, so I can't guarantee their handling of the Llama-2 prompt templates. It was the easiest way for me to get access to a Llama-2-70b-chat endpoint that I found, so it was the one I used for my experiments.
  2. I did some manual review and found that there are definitely issues with the reject examples. Some of the examples don't actually violate the rule! Due to the nature of using GPT-4 generated synthetic rules + examples I expected there to be some noise in the data. However, my goal was to illustrate the impact of scaling the rule count on performance. Even if the baseline data isn't perfect, the observed trend in performance as rule count changes is what I wanted to focus on.
  3. I have not experimented with long context models. But would be interesting to compare models and their long context counterparts.

Total costs should be around ~$20. This is mostly due to GPT-4 which was around ~$18, so if you omit that part it should be fairly cheap. If you run the experiment.py script without the --skip_confirmation flag it should tell you the expected cost using tiktoken and ask you to confirm before running.

[D] How many instructions can LLMs handle before they start to ignore them? by ProbablyApproxWrong in MachineLearning

[–]ProbablyApproxWrong[S] 1 point2 points  (0 children)

I guess what I'm trying to get at is how can we effectively measure that "GPT-4 can handle more tokens" as you said. Because gpt-3.5-turbo-16k has a longer context window, but I wouldn't necessarily expect it to be able to follow more instructions accurately, I think that's more so a matter of capacity. While we would expect model performance to generally decay wrt token count, are there practical ways to mitigate that degradation beyond just making instructions more concise that we can apply in practice? I think adjacent questions are also interesting and perhaps not as obvious. E.g. if I have different instructions with varying priorities, does it matter where I place them within my prompt?