Use cases for multi agents?
Autogen: Enable next-gen large language model applications
31–40 of 59 posts
Re: Autogen: Enable next-gen large language model applications
#32A question for people researching LLMs and their capabilities: Is there any reason to believe that the interaction of multiple agents (using the same model) will yield some emergent property that is beyond the capabilities of the agent model? I'm not working with LLMs, but my intuition is that whatever these multi agent setups come up with could also be achieved by a single agent just talking to itself, as they all a…
It's possible that they can only "wear so many hats" at the same time.
And that is where MoE comes in with some more advancements in routing
Re: Autogen: Enable next-gen large language model applications
#33It doesn’t help you inherently solve the problem per se, but what it does allow you to do that is distinctive is keep the human and the loop that can assist the agents to solve problems.
To some degree it can also keep problems in the logic chain from snowballing, and causing the overall objective to fail because there’s invalid logic in the sequence
Re: Autogen: Enable next-gen large language model applications
#34A bunch of single-comment related threads. Others?
AutoGen: A Multi-Agent Framework for Streamlining Task Customization - https://news.ycombinator.com/item?id=37855314 - Oct 2023 (1 comment)
Microsoft's AutoGen – Guide to code execution by LLMs - https://news.ycombinator.com/item?id=37822809 - Oct 2023 (1 comment)
Making memes with Autogen AI (open source LLM agent framework) [video] - https://news.ycombinator.com/item?id=37750897 - Oct 2023 (1 comment)
AutoGen: Enabling next-generation large language model applications - https://news.ycombinator.com/item?id=37647404 - Sept 2023 (1 comment)
AutoGen: Enabling Next-Gen GPT-X Applications - https://news.ycombinator.com/item?id=37220686 - Aug 2023 (1 comment)
Re: Autogen: Enable next-gen large language model applications
#35Earlier quoted context omitted.
Since a single inference is limited by context length, a multiple agents model is able to process more context at each steps of the reasoning chain, which might improve the overall quality. However, given how easy it is getting to fine tune models, it's likely that multi-agent models will make a lot of sense to split the workload and assign each part to a specialized agent.
> a single inference is limited by context length, Yes. > multiple agents model is able to process more context at each steps of the reasoning chain What? How can a multi agent model have more context at a single step? The single step runs on a single agent. It would literally the same as a single agent? The multi agent approach is simply packaging up different “personas” for single steps; and yes, it is entirely rea…
Their comment uses two (valid) context lengths: "organizational total" and "single agent." The latter is a subset of the former.
By analogy: no agent can summarize War and Peace, but several agents can, Peace-wise (sorry). Like AI map reduce. The question is thus "why not use one agent for this recursive merger?" Answers maybe being:
1. Different scholars (Russian lit. agents, ...war strategists?, etc) pay attention to different things with valuable insights
2. Multiple readers parallelize well, and some are faster than others
3. Managers can direct talent to (re)read chapters most relevant to their specialties, and coordinate meta-learning and communication
You might not get much mileage out of this approach with book summaries, but other domains are a different story (sorry).
Re: Autogen: Enable next-gen large language model applications
#36Unless I'm missing something, how is this library different from prompting a single chatbot: "Write a dialog in which A, B, and C, each playing a different role, have a conversation and do something D"?
You can have the character description more front and center, if that makes any sense.
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.
Re: Autogen: Enable next-gen large language model applications
#37A question for people researching LLMs and their capabilities: Is there any reason to believe that the interaction of multiple agents (using the same model) will yield some emergent property that is beyond the capabilities of the agent model? I'm not working with LLMs, but my intuition is that whatever these multi agent setups come up with could also be achieved by a single agent just talking to itself, as they all a…
Mixture of experts: Make each model world-class within a single domain.
If adding one more common-sense QnA makes the calculus-bot even slightly worse at caculus, don't do it.
Re: Autogen: Enable next-gen large language model applications
#38This just reminds me: I have been wondering, if you get multiple instances of GPT-4 talking to each other, each seeded with a different personality prompt, do they have interesting conversations? I suspect it would devolve in to nonsense quickly, but I’ve never seen any chat log of two GPT instances talking. Does anyone have a reference for this? Thanks.
Re: Autogen: Enable next-gen large language model applications
#39Earlier quoted context omitted.
It isn't too bad; naming stuff is always hard. If the Microsoft marketers knew about it then I would expect to see Azure™ Gen.NET™ Live™.
Did they give up on ONE?
>> https://learn.microsoft.com/en-us/training/modules/examine-c...
OneLake is Fabric's lake-centric architecture that provides a single, integrated environment for data professionals and the business to collaborate on data projects. Think of it like OneDrive for data; OneLake combines storage locations across different regions and clouds into a single logical lake, without moving or duplicating data. Data can be stored in any file format in OneLake and can be structured or unstructured. For tabular data, the analytical engines in Fabric will write data in delta format when writing to OneLake. All engines will know how to read this format and treat delta files as tables no matter which engine writes it.
Re: Autogen: Enable next-gen large language model applications
#40This just reminds me: I have been wondering, if you get multiple instances of GPT-4 talking to each other, each seeded with a different personality prompt, do they have interesting conversations? I suspect it would devolve in to nonsense quickly, but I’ve never seen any chat log of two GPT instances talking. Does anyone have a reference for this? Thanks.
easy enough to test, copy and paste the responses after initial prompt