One frustration I've had with all this mixture-of-experts research:
Randomized Algorithms 101 - or basic stochastic reasoning - suggests that if the temperature parameter is > 0, querying an LLM N times and picking the majority result (perhaps with an N+1th query to the LLM) will generally result in better performance than asking it once and choosing that result.
It seems plausible to me that the gains can be further improved with a specialized mixture of different LLMs (which could then be run at temp = 0), or by finding better ways to break tasks into subtasks as this paper suggests. But AFAICT nobody has done anything to actually quantify these hypothetical gains versus the dumb randomized algorithm approach! In particular there might be voting strategies or mixtures - even specific models - where MoE/etc is strictly worse than naive repetition.
I am a concerned citizen w.r.t LLMs rather than a researcher, so I might be missing something. It just seems odd that LLM researchers forgot the first chapter of Motwani/Raghavan.
Randomized Algorithms 101 - or basic stochastic reasoning - suggests that if the temperature parameter is > 0, querying an LLM N times and picking the majority result (perhaps with an N+1th query to the LLM) will generally result in better performance than asking it once and choosing that result.
It seems plausible to me that the gains can be further improved with a specialized mixture of different LLMs (which could then be run at temp = 0), or by finding better ways to break tasks into subtasks as this paper suggests. But AFAICT nobody has done anything to actually quantify these hypothetical gains versus the dumb randomized algorithm approach! In particular there might be voting strategies or mixtures - even specific models - where MoE/etc is strictly worse than naive repetition.
I am a concerned citizen w.r.t LLMs rather than a researcher, so I might be missing something. It just seems odd that LLM researchers forgot the first chapter of Motwani/Raghavan.