Autogen: Enable next-gen large language model applications

And the chess one: https://github.com/microsoft/autogen/blob/main/notebook/agen...

at the very least, you can get things done that would be extremely difficult or practically impossible to do with a single instance.

I think of agents more or less as python classes with a mixture of natural language and code functions. You design them to do something with information they produce, and to interface with other agents or “tools” in some way.

But all the agents can be the same language model under the hood, they are frames used to build different kinds of contexts.

And yes I think the idea is that emergent behaviour can be useful. This comes to mind

https://github.com/MineDojo/Voyager

But I think we are still a small ways off from being really smart about agents. My opinion is that we haven’t quite figured out what we are doing yet.

When you give it a specific role it essentially hones in on the relevant part of the training data. Researcher in X field? Papers from that field get priority in formulating responses and the accuracy of token prediction for contextually relevant tasks goes up.

OTOH, if you try to go 'meta' - ie. you give it a scenario where it imagines a group of scholars chatting with each other, then it hones in on situations where there is a dialogue amongst a group (ie. a play/script).

If you write a short story it's often better to split it into parts (make an outline, write the story, edit the story) than if you would try to do the whole process at once. The same can be true for LLMs I suppose.

In an LLM sense this would be like the different system prompts are sampling different parts of the training distribution, but I'm not able to validate such a claim or know if someone has validated it before.

https://en.wikipedia.org/wiki/Mixture_of_experts

* sometimes we want an LLM with longer context, faster speed, higher quality, etc: so even in a model family, in the same job, diff model configs

* we do a lot of prompt tuning for agent calls, like what a good Splunk query is, what SQL tables are currently available, what a good chart is, how to using a graph library, ...

* we also do accompanying code-level work, like running a generated python data analysis in a sandbox and feeding back exceptions to the LLM, or checking for parse errors when running a DB query, which feed back to the LLM

* When working directly on data, we might run it through the LLM, which might get into parallel chunked calls, a summary tree, etc, where a single LLM call would be insufficient, costly, slow, etc

So instead of diluting attention across three separate character descriptions, your model will see just the chat log and the single description of the persona it should respond from. This may or may not make a difference.