Comment by blobbers
4 days ago
I'm curious if there is measurable value in diversity of thought, and if there's diminishing returns on a single models thought pattern.
For example, compute X tokens with model A, then feed those into model B, etc. to get chain of thought through a diverse set of mdoels rather than chain of thought through a heterogeneous chain.
Humans seem to strongly believe echo chambers are bad. Are LLMs the same?
there are and this what you optimize for. ensemble learning has a long literature on this. you want models that have the most diverse pool of capabilities so they complement each other. in verifiable tasks or classification this is straightfroward but a bit tricker in open ended text gen or CoT. this is one of our core research question
My experience is mostly around either ensembles of weak learners that are bootstrapped to create a strong learner, or around boosted learners where you're training on residuals. With CoT you're sort of adding entropy to your answer, but you're also possibly getting a more thorough answer?
I'm curious though if these training methods are convergent or are models actually different; just like how in the stock market people think they're "diversified" but the truth is their exposure is likely much more risk correlated than one might think.
In certain situations, one right answer is better than a committee discussing the problem, but in others its sometimes nice to have some alternative methods of solving something. Fun project nonetheless.
My approach to using multiple models has been less about CoT but more about time to first token, and how you can use a small model to start interacting with the user while in parallel the more complex model is building a larger more complex thought. My work on this was primarily for voice backed interfaces before the voice models became quite a lot faster.
i think that’s exactly the core of the question. There is meaningful decorrelation across models in some domains, like language for instance. but i agree that it’s much less obvious for reasoning.
I don’t think more models is necessarily better, and one of our research questions is also: when does adding another reasoning path actually add useful information, and when is a single strong path better than an ensemble?
The models are all converging, there is no diversity in any single generation.