4 ms·

How about a local wikipedia dump, with precalculated embeddings? Then you can perform a similarity search first and feed the results to the LLM. It’s less like

How about a local wikipedia dump, with precalculated embeddings? Then you can perform a similarity search first and feed the results to the LLM. It’s less likely to hallucinate this way.

jacquesm 3y ago
That would make a lot more sense. That way at least you have a chance to check up on the output, lest your first meal of 'Hedysarum alpinum' ends up being your last.
- bravura 3y ago
  The key aspect to this being a good solution is actually building a corpus representing as much possible reference knowledge needed in scenario. The idea that the answer is Wikipedia way underestimates the scope. The Wikipedia patch doesn’t make much sense to me. What percent of the important questions being asked in this doomsday scenario actually have their answer in Wikipedia? If 50% of the time you are left trusting raw LLaMa, then you don’t really have a decent solution. I do appreciate the sentiment tho that future or finetuned LLMS might fit on an RPi or whatever, and be good enough.
  - hnuser123456 3y ago
    I think the theory is that an LLM can integrate the knowledge of Wikipedia and become something greater than the sum of its parts by applying reasoning that is explained in one article to situations in other topics where the reasoning might not be so well explained. Then you can ask naive questions in new scenarios where you might not have the background knowledge (or simply the mental energy) to figure out a right answer to a situation on your own but it powers through for you. AFAIK current LLMs are not this abstract. If one type of reasoning is more often applied in one scenario and another type of reasoning is applied to another, they don't have any context beyond the words and they know what topics humans usually jump to from other given topics.
yu3zhou4 3y ago
It would be great to see such project implemented, I wonder how good would it perform
- jacquesm 3y ago
  Yes, especially having fact checked output of LLMs would be a nice step in the right direction. Throwing out the hallucinated bits and keeping the good stuff would make LLMs a lot more applicable.
  - eis 3y ago
    Isn't that a bit of a holy grail though? If your software can fact check the output of LLMs and prevent hallucinations then why not use that as the AI to get the answers in the first place?
    - jacquesm 3y ago
      Because you - hopefully - have a check against something that is on average of higher quality than the combined input of an LLM. I'm not sure if this can work or not but it would be nice to see a trial, you could probably do this by hand if you wanted to by breaking up the answer from an LLM into factoids and then to check each of those individually, and to assign a score to them based on the amount of supporting evidence for the factoid. I'd love that as a plug-in to a browser too.
  - regularfry 3y ago
    https://arxiv.org/pdf/2308.04430.pdf https://arxiv.org/pdf/2308.04430.pdf is interesting from that point of view. They've tackled it from the perspective of avoiding copyright content in training, but including it in inference but I think it ought to mean less hallucination because they also (claim to) solve the attribution problem.
    - jacquesm 3y ago
      Nice one, thank you. Added to my 'read later today' list, the abstract looks very interesting.
chank 3y ago
this concept already exists and is in practice at many companies that require knowledge driven results. https://arxiv.org/abs/2005.11401 https://arxiv.org/abs/2005.11401
- regularfry 3y ago
  There was another paper out recently that adds to this: https://arxiv.org/pdf/2308.04430.pdf https://arxiv.org/pdf/2308.04430.pdf. Looks like a more flexible approach to document storage, and it outperforms retrieval in context. They trained up their own LLM, but from the text it seems like it might be possible to use any LLaMA-style LM without retraining. Not sure though, need to give it a proper look.
deleted 3y ago
[deleted]
mdp2021 3y ago
> a local wikipedia dump There exists (at least) a project to train and query an LLM on local documents: privateGPT - https://github.com/imartinez/privateGPT https://github.com/imartinez/privateGPT It should provide links to the the source with the relevant content, to check the exact text: > You'll need to wait 20-30 seconds (depending on your machine) while the LLM model consumes the prompt and prepares the answer. Once done, it will print the answer and the 4 sources it used as context from your documents You will have noticed, in that first sentence, that it may not be practical, especially on an Orange Pi.

How about a local wikipedia dump, with precalculated embeddings? Then you can perform a similarity search first and feed the results to the LLM. It’s less likely to hallucinate this way.

jacquesm 3y ago

That would make a lot more sense. That way at least you have a chance to check up on the output, lest your first meal of 'Hedysarum alpinum' ends up being your last.

bravura 3y ago
The key aspect to this being a good solution is actually building a corpus representing as much possible reference knowledge needed in scenario. The idea that the answer is Wikipedia way underestimates the scope. The Wikipedia patch doesn’t make much sense to me. What percent of the important questions being asked in this doomsday scenario actually have their answer in Wikipedia? If 50% of the time you are left trusting raw LLaMa, then you don’t really have a decent solution. I do appreciate the sentiment tho that future or finetuned LLMS might fit on an RPi or whatever, and be good enough.
- hnuser123456 3y ago
  I think the theory is that an LLM can integrate the knowledge of Wikipedia and become something greater than the sum of its parts by applying reasoning that is explained in one article to situations in other topics where the reasoning might not be so well explained. Then you can ask naive questions in new scenarios where you might not have the background knowledge (or simply the mental energy) to figure out a right answer to a situation on your own but it powers through for you. AFAIK current LLMs are not this abstract. If one type of reasoning is more often applied in one scenario and another type of reasoning is applied to another, they don't have any context beyond the words and they know what topics humans usually jump to from other given topics.

yu3zhou4 3y ago

It would be great to see such project implemented, I wonder how good would it perform

jacquesm 3y ago
Yes, especially having fact checked output of LLMs would be a nice step in the right direction. Throwing out the hallucinated bits and keeping the good stuff would make LLMs a lot more applicable.
- eis 3y ago
  Isn't that a bit of a holy grail though? If your software can fact check the output of LLMs and prevent hallucinations then why not use that as the AI to get the answers in the first place?
  - jacquesm 3y ago
    Because you - hopefully - have a check against something that is on average of higher quality than the combined input of an LLM. I'm not sure if this can work or not but it would be nice to see a trial, you could probably do this by hand if you wanted to by breaking up the answer from an LLM into factoids and then to check each of those individually, and to assign a score to them based on the amount of supporting evidence for the factoid. I'd love that as a plug-in to a browser too.
- regularfry 3y ago
  https://arxiv.org/pdf/2308.04430.pdf https://arxiv.org/pdf/2308.04430.pdf is interesting from that point of view. They've tackled it from the perspective of avoiding copyright content in training, but including it in inference but I think it ought to mean less hallucination because they also (claim to) solve the attribution problem.
  - jacquesm 3y ago
    Nice one, thank you. Added to my 'read later today' list, the abstract looks very interesting.

chank 3y ago

this concept already exists and is in practice at many companies that require knowledge driven results. https://arxiv.org/abs/2005.11401 https://arxiv.org/abs/2005.11401

regularfry 3y ago
There was another paper out recently that adds to this: https://arxiv.org/pdf/2308.04430.pdf https://arxiv.org/pdf/2308.04430.pdf. Looks like a more flexible approach to document storage, and it outperforms retrieval in context. They trained up their own LLM, but from the text it seems like it might be possible to use any LLaMA-style LM without retraining. Not sure though, need to give it a proper look.

deleted 3y ago

[deleted]

mdp2021 3y ago

> a local wikipedia dump There exists (at least) a project to train and query an LLM on local documents: privateGPT - https://github.com/imartinez/privateGPT https://github.com/imartinez/privateGPT It should provide links to the the source with the relevant content, to check the exact text: > You'll need to wait 20-30 seconds (depending on your machine) while the LLM model consumes the prompt and prepares the answer. Once done, it will print the answer and the 4 sources it used as context from your documents You will have noticed, in that first sentence, that it may not be practical, especially on an Orange Pi.