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Sentdex 
posted an update Feb 20
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Working through the Reddit dataset, one thing that occurs to me is we pretty much always train LLMs to be a conversation between 2 parties like Bot/Human or Instruction/Response.

It seems far more common with internet data that we have multi-speaker/group discussions with a dynamic number of speakers. This also seems to be more realistic to the real world too and requires a bit more understanding to model.

Is there some research into this? I have some ideas of how I'd like to implement it, but I wonder if some work has already been done here?
Dewa

Aya-101 can help

pranav-deshpande

I was thinking exactly the same thing when ChatGPT first came out! I have run some minor experiments with causal language modeling by having a fixed number of users/speakers and then instruct fine-tuning the base/foundational model. "Dynamic number of speakers" sounds interesting, though! Maybe there is a clever way to inject new tokens into the vocabulary to achieve this.

Would love to contribute tothis initiative.

eliotz

I would imagine a method similar to Mistral's router could work (RL policy rewarding equal distribution between models)

Also there's this paper from pre-LLM craze that might be helpful. Would be interesting to see it implimented with more powerful language models

https://arxiv.org/pdf/1907.05507.pdf

D4ve-R

This paper Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models did some research into this.

Tonic

i've had "best" results mushing everything into a single context window with a single "final"/"next" answer , i think i remember @teknium saying they often do that and they may have published that research , but i cant speak for them, i just remember them saying that and feeling validated :-)

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