3 ms·

> In cases where you ask GPT-3 questions that have a clear correct answer, I think you can use the probability to judge how correct the answer is. For example,

> In cases where you ask GPT-3 questions that have a clear correct answer, I think you can use the probability to judge how correct the answer is. For example, when asking "How tall is Mount Everest?" I would want the completion "Mount Everest is ____ meters above sea level." to have a very high probability for the ____ tokens. Maybe, as long as you're aware that this is the same kind of correctness that you get from looking at Google's first search results (the old kind of organic pages, not the "knowledge graph", which uses an different process - precisely to avoid being spammed by SEO) i.e. "correctness by popularity". This means that the content that is more replicated will be considered more true by the system, regardless of its connection to reality or its coherence with the rest of the knowledge in the system. And you know what they say about big enough lies that you keep repeating millions of times.

ailef 4y ago
> This means that the content that is more replicated will be considered more true by the system, regardless of its connection to reality or its coherence with the rest of the knowledge in the system. I understand the problem, but what better way do we currently have to measure its connection to reality? At least from a practical point of view it seems that LLMs have achieved way better performance than other methods in this regard, so repeatedness doesn't look like that bad a metric. Or rather, it's the best I think we currently have.
- mannykannot 4y ago
  > I understand the problem, but what better way do we currently have to measure its connection to reality? We can consider its responses to a broader range of questions than those having an unambiguous and well-known answer. Its propensity for making up 'facts', and for fabricating 'explanations' that are incoherent or even self-contradictory shows that any apparent understanding of the world being represented in the text is illusory.
mannykannot 4y ago
I agree, and furthermore, a search engine is constrained to pick its responses from what's already out there. This line of thought is a distraction, anyway. The likelehood that GPT-3 will do as well as a search engine on topics where there is an unambiguous and well-known answer does little to address the more general concern.

ailef 4y ago

> This means that the content that is more replicated will be considered more true by the system, regardless of its connection to reality or its coherence with the rest of the knowledge in the system. I understand the problem, but what better way do we currently have to measure its connection to reality? At least from a practical point of view it seems that LLMs have achieved way better performance than other methods in this regard, so repeatedness doesn't look like that bad a metric. Or rather, it's the best I think we currently have.

mannykannot 4y ago
> I understand the problem, but what better way do we currently have to measure its connection to reality? We can consider its responses to a broader range of questions than those having an unambiguous and well-known answer. Its propensity for making up 'facts', and for fabricating 'explanations' that are incoherent or even self-contradictory shows that any apparent understanding of the world being represented in the text is illusory.

mannykannot 4y ago

I agree, and furthermore, a search engine is constrained to pick its responses from what's already out there. This line of thought is a distraction, anyway. The likelehood that GPT-3 will do as well as a search engine on topics where there is an unambiguous and well-known answer does little to address the more general concern.