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Thank you for your analysis, it's great to have the insights of someone with both legal and ML backgrounds. I want to point out that a big part of building any

Thank you for your analysis, it's great to have the insights of someone with both legal and ML backgrounds. I want to point out that a big part of building anything on GPT-3 (or other large LMs for that matter) is "prompt engineering", which means you try out thousands of prompts and sampling parameters until you find something that works reasonably well for your use case. Taking two default templates and a few different temperatures is like taking some tutorials for a new framework, building a proof of concept from them, then making a judgement from that. Sure, it can provide a good first assessment, but that's it. You would need much deeper experience to come to a meaningful conclusion.

NovemberWhiskey 6y ago
>I want to point out that a big part of building anything on GPT-3 (or other large LMs for that matter) is "prompt engineering", which means you try out thousands of prompts and sampling parameters until you find something that works reasonably well for your use case. As someone who is instinctively skeptical about these language models, this kind of statement makes my antennae twitch. You have this black box model that generates all sort of plausible outputs, you jiggle the handle until those outputs meet your expectations for some range of tested inputs, and then ... you assume it's just going to work? For parlor tricks, or even low-stakes real world activities that might be enough - but how can you trust it?
- make3 6y ago
  (As one of them) every professional researcher in NLP at every large company (incl me) knows you can't rely on generation right now, and huge teams everywhere are working on reliability in text generation
  - joe_the_user 6y ago
    So, you take this "general purpose model" (with a huge corpus of standard text) and you attempt to use it for a narrow purpose. The model requires a lot of prompt tweaking and other things for this narrow model but eventually you "make it work". How do you know you're not just "programming" a chatbot (by indirectly filtering for the text-pieces you want) but in the most indirect and unguaranteed fashion possible? I suppose the advantage is you can say "look, it's intelligent".

NovemberWhiskey 6y ago

>I want to point out that a big part of building anything on GPT-3 (or other large LMs for that matter) is "prompt engineering", which means you try out thousands of prompts and sampling parameters until you find something that works reasonably well for your use case. As someone who is instinctively skeptical about these language models, this kind of statement makes my antennae twitch. You have this black box model that generates all sort of plausible outputs, you jiggle the handle until those outputs meet your expectations for some range of tested inputs, and then ... you assume it's just going to work? For parlor tricks, or even low-stakes real world activities that might be enough - but how can you trust it?

make3 6y ago
(As one of them) every professional researcher in NLP at every large company (incl me) knows you can't rely on generation right now, and huge teams everywhere are working on reliability in text generation
- joe_the_user 6y ago
  So, you take this "general purpose model" (with a huge corpus of standard text) and you attempt to use it for a narrow purpose. The model requires a lot of prompt tweaking and other things for this narrow model but eventually you "make it work". How do you know you're not just "programming" a chatbot (by indirectly filtering for the text-pieces you want) but in the most indirect and unguaranteed fashion possible? I suppose the advantage is you can say "look, it's intelligent".