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BullockHouse t1_jduba6v wrote

Keeping a second GPT-4 window open and asking it to verify information from the first seems to work pretty well. The models fail by guessing when uncertain, but they have no incentive to cooperate and back up one another's guesses. The problem with your solution is that it probably biases the model towards making up some papers just to fit the prompt and have a mix.

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master3243 t1_jdue84p wrote

> The problem with your solution is that it probably biases the model towards making up some papers just to fit the prompt and have a mix.

That's a very important point, adding an extra condition (if 'p' then 'q') to the prompt makes the model biased towards doing 'p' then doing 'q' to fulfil the prompt despite the condition still being met if it just avoided doing 'p'.

For a more concrete example, here's me asking ChatGPT to write two essays:

1- Write a paragraph about zoos. Figure. (Notice how no Elephants are mentioned)

2- Write a paragraph about zoos with (if 'p' then 'q') condition. Figure (Notice how only this answer mentions Elephants)

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WarAndGeese t1_jdubx7q wrote

Also if the second neural network is running as a separate internet-connected application, it can go out and verify the output of the first, send back its results, and tell the first to either change or remove each paper that it cannot find and verify. The second neural network can make errors as well, but through these interconnected systems errors can be reduced somewhat largely.

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tt54l32v t1_jdvlsg3 wrote

Ok so how does one simplify that? Also why does it have to be separate? Genuinely curious.

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WarAndGeese t1_jdy5z29 wrote

I'll call them applications rather than neural networks or LLMs for simplicity.

The first application is just what OP is doing and what people are talking about in this thread, that is, asking for sources.

The second application has access to research paper databases, through some API presumably. For each answer that the first application outputs, the second answer queries it against the databases. If it gets a match, it returns a success. If it does not find the paper (this could be because it doesn't exist or becauase the title was too different from that of a real paper, either case is reasonable) it outputs that it was not found. For each paper that was not found, it outputs "This paper does not exist, please correct your citation". That output is then fed back into the first application.

Now, this second application could be a sort of database query or it could just consist of a second neural network being asked "Does this paper exist?". The former might work better but the latter would also work.

The separation is for simplicity's sake, I guess you can have one neural network doing both things. As long as each call to the neural network is well defined it doesn't really matter. The neural network wouldn't have memory between calls so functionally it should be the same. Nevertheless I say two in the same way that you can have two microservices running on a web application. It can be easier to maintain and just easier to think about.

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tt54l32v t1_jdyc1h3 wrote

So the second app might would fare better leaning towards search engine instead of LLM but some LLM would ultimately be better to allow for less precise matches of specific set of searched words.

Seems like the faster and more seamless one could make this, the closer we get to agi. To create and think it almost needs to hallucinate and then check for accuracy. Is any of this already taking place in any models?

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