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It’s sort of amusing to me how you feel your analysis is more correct than sentence-transformers or whichever embedding algorithm was used. I think to most peo

It’s sort of amusing to me how you feel your analysis is more correct than sentence-transformers or whichever embedding algorithm was used. I think to most people it’s pretty obvious you are trying to make the algorithm fit your bias/preconceived ideas.

bugglebeetle 3y ago
It’s sort of amusing to me that you think sentence-transformers is better at semantic similarity than just about any human. This is hardly an example of bias, but a perfect example of the limits of a design meeting real-world user testing. To quote the joke/meme: A software tester walks into a bar. Runs into a bar. Crawls into a bar. Dances into a bar. Flies into a bar. Jumps into a bar. And orders: a beer. 2 beers. 0 beers. 99999999 beers. a lizard in a beer glass. -1 beer. "qwertyuiop" beers. Testing complete. A real customer walks into the bar and asks where the bathroom is. The bar goes up in flames.
- dmbche 3y ago
  I'm stealing the joke
- linuxdude314 3y ago
  Generally speaking I don’t think it is. When people agree with the results for the majority of the corpus but cherry-pick “inaccuracies” to justify their bigotry, that’s what I have a problem with. It sounds like you want to RLHF a model to hate LGBTQ people… This is not progress.
  - bugglebeetle 3y ago
    I’m confused by this. Christianity, for almost all of its history and in the present, espouses this bigotry as its doctrine, justifying it with the handful of references to homosexuality in its texts. What should be returned by such a search if not these sections? Some specific textual interpretation that elides this reality?
    - dragonwriter 3y ago
      > Christianity, for almost all of its history and in the present, espouses this bigotry as its doctrine, justifying it with the handful of references to homosexuality in its texts. What should be returned by such a search if not these sections? Its a search of the text by the semantics of the text, not a search of the text by how doctrine has been rationalized (which could be done by an LLM, but wouldn't use a vector DB of the text, rather, it would need sonething like a vector DB of documentation of relevant written justifications of doctrine annotated with scriptural references, and then a simple book-chapter-verse DB of the scriptural text.) These are decidedly different problems, and complaining that something that purports to solve the first doesn’t solve the second is... odd.
valyagolev 3y ago
Excited for the next 100 years for this AI-related equivalent of "you got the wrong result, because your query is slightly not exactly the way system expects it to be". ChatGPT knows a good answer to the question even without embeddings. But this particular tool can't replicate it, and says e.g. "In summary, these verses highlight sexual ethics and various sins in general, but do not uniformly condemn or condone homosexuality specifically." (which is not wrong, it's just the wrong verses found). (It gives a different summary on different tries) This is a common problem with embedding search. Obviously, the other traditional techniques would be even worse. But I'd love the systems to be better, and I propose a potential solution, and ask for other ones. I will not be content with your "put up with AI idiosyncrasies and weaknesses, as if they were the real actual conceptual limitation of knowledge" approach. AI has potential to create great UI, but your attitude won't help with that
- dragonwriter 3y ago
  > ChatGPT knows a good answer to the question even without embedding ChatGPT knows what people in its training corpus commonly say is a good answer. When you force it to look strictly at the text isolating the influence of popular interpretation, it comes up with a different answer that you like less. Now, one explanation could be that the embeddings it uses to find relevant text are bad. But there’s another explanation, too...

bugglebeetle 3y ago

It’s sort of amusing to me that you think sentence-transformers is better at semantic similarity than just about any human. This is hardly an example of bias, but a perfect example of the limits of a design meeting real-world user testing. To quote the joke/meme: A software tester walks into a bar. Runs into a bar. Crawls into a bar. Dances into a bar. Flies into a bar. Jumps into a bar. And orders: a beer. 2 beers. 0 beers. 99999999 beers. a lizard in a beer glass. -1 beer. "qwertyuiop" beers. Testing complete. A real customer walks into the bar and asks where the bathroom is. The bar goes up in flames.

dmbche 3y ago
I'm stealing the joke
linuxdude314 3y ago
Generally speaking I don’t think it is. When people agree with the results for the majority of the corpus but cherry-pick “inaccuracies” to justify their bigotry, that’s what I have a problem with. It sounds like you want to RLHF a model to hate LGBTQ people… This is not progress.
- bugglebeetle 3y ago
  I’m confused by this. Christianity, for almost all of its history and in the present, espouses this bigotry as its doctrine, justifying it with the handful of references to homosexuality in its texts. What should be returned by such a search if not these sections? Some specific textual interpretation that elides this reality?
  - dragonwriter 3y ago
    > Christianity, for almost all of its history and in the present, espouses this bigotry as its doctrine, justifying it with the handful of references to homosexuality in its texts. What should be returned by such a search if not these sections? Its a search of the text by the semantics of the text, not a search of the text by how doctrine has been rationalized (which could be done by an LLM, but wouldn't use a vector DB of the text, rather, it would need sonething like a vector DB of documentation of relevant written justifications of doctrine annotated with scriptural references, and then a simple book-chapter-verse DB of the scriptural text.) These are decidedly different problems, and complaining that something that purports to solve the first doesn’t solve the second is... odd.

valyagolev 3y ago

Excited for the next 100 years for this AI-related equivalent of "you got the wrong result, because your query is slightly not exactly the way system expects it to be". ChatGPT knows a good answer to the question even without embeddings. But this particular tool can't replicate it, and says e.g. "In summary, these verses highlight sexual ethics and various sins in general, but do not uniformly condemn or condone homosexuality specifically." (which is not wrong, it's just the wrong verses found). (It gives a different summary on different tries) This is a common problem with embedding search. Obviously, the other traditional techniques would be even worse. But I'd love the systems to be better, and I propose a potential solution, and ask for other ones. I will not be content with your "put up with AI idiosyncrasies and weaknesses, as if they were the real actual conceptual limitation of knowledge" approach. AI has potential to create great UI, but your attitude won't help with that

dragonwriter 3y ago
> ChatGPT knows a good answer to the question even without embedding ChatGPT knows what people in its training corpus commonly say is a good answer. When you force it to look strictly at the text isolating the influence of popular interpretation, it comes up with a different answer that you like less. Now, one explanation could be that the embeddings it uses to find relevant text are bad. But there’s another explanation, too...