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> Is this similar to the effect that I have seen when you have two different LLMs talking to each other, they tend to descend into nonsense ? Is that really tr
by diggan 2y ago
> Is this similar to the effect that I have seen when you have two different LLMs talking to each other, they tend to descend into nonsense ?
Is that really true? I'd expect that with high temperature values, but otherwise I don't see why this would happen, and I've experimented with pitting same models against each other and also different models against different models, but haven't come across that particular problem.
- reportgunner 2y agoCan you show examples ? In any AI related discussions there are only some claims by people and never examples of the AI working well.
- whimsicalism 2y agoyou’re saying you have never seen an example of AI working well?
- reportgunner 2y agoYeah, can you show me ?
- roboboffin 2y agoI think this is similar to this point: https://news.ycombinator.com/item?id=41601738 https://news.ycombinator.com/item?id=41601738 That the chain-of-thought diverges from accepted truth as an incorrect token pushes it into a line of thinking that is not true. The use of RL is there to train the LLM to implement strategies to bring it back from this. In effect, two LLMs would be the same and would slow diverge into nonsense. Maybe it is something that is not so much of a problem anymore. Yann LeCun talks about how the correct way to fix this is to use an internal consistent model of the truth; then the chain-of-thought exists as a loop within that consistent model meaning it cannot diverge. The language is a decoded output of this internal model resolution. He speaks about this here: https://www.youtube.com/watch?v=N09C6oUQX5M https://www.youtube.com/watch?v=N09C6oUQX5M Anyway, that's my understanding. I'm no expert.