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> If we consider that GPT-4 is about as capable as a reasonably intelligent high school graduate or college freshmen, I wonder how much actual benefit we get fr

by Closi 3y ago

> If we consider that GPT-4 is about as capable as a reasonably intelligent high school graduate or college freshmen, I wonder how much actual benefit we get from multi-agent setups. It's worth noting that GPT-4 internally uses a Mixture of Experts (MoE) model with 8 'experts' internally, so it's more similar to a multi-agent setup than you might think initially.

capableweb 3y ago
> It's worth noting that GPT-4 internally uses a Mixture of Experts (MoE) model with 8 'experts' internally Has this actually been confirmed, either officially by OpenAI or otherwise? As far as I know, George Hotz claimed this once, and since then everyone just assumed it was the truth without actually waiting for any sort of verification.
- sheepscreek 3y ago
  TL;DR This might work - but it will be like watching Groundhog Day. It will require many iterations, make too many mistakes to get there and won’t remember a thing. My naive understanding of layers in a model is that each layer loosely acts as an expert in one step of the entire process. For example, in an object recognition model, one layer takes on the task of separating objects from the background, another excels at knowing the colour of different things, another might learn the difference between a blue sky vs. the colour sky blue. So essentially, we’re trying to mimic the same working model at a higher level of abstraction. Similar to how our body is made of atoms. Many atoms make a molecule. Many molecules make organic tissue, and amino acids that perform more complex operations. Skip all the way up and you have a human being. Human beings in numbers can put a man on the moon, create anti-matter and nuclear explosions. In theory - this can work just as well. One agent to provide a solution, another to critique it, another to verify it, another to mimic the end-user. The big obvious missing piece is memory and the ability to learn while doing (at least in existing LLMs). It’s like having a software development team that’s frozen in time and knowledge. These LLM agents will always require some micromanagement and hand holding, and will waste a lot of resources with failed attempts - that they are going to repeat every time you ask them to perform this task.
zoogeny 3y ago
Right, but the intuition I am going for isn't "Are 10 high-school students who collaborate together smarter than 1 high-school student working on their own". The intuition I am asking for is more like: "Are 10, 100, or 1000 GPT-3s capable of producing the equivalent intelligence of 1 GPT-4". That kind of reasoning expanded to GPT-N. And further, if no number of GPT-3s can reach the level of intelligence of a GPT-4, then what set of problems can be solved by 100s of GPT-3s in a mixed model and what problems require the single GPT-4.
- visarga 3y ago
  You can generally amplify models by using more tokens, more calls or relying on some oracle of correctness. I see rapid growth of synthetic datasets from now on, we need the diversity of synthetic data to progress further.
lordofgibbons 3y ago
> It's worth noting that GPT-4 internally uses a Mixture of Experts (MoE) model with 8 'experts' internally, so it's more similar to a multi-agent setup than you might think initially. That's a very common misunderstanding of what MoEs are. What you're describing is an Ensemble. A MoE is where a the input is "routed" to one of X "experts" in the higher layers of the network. Something like this: {input} -> [lower layers] -> <decide which upper layers to send input to> -> [expert] -> output And these experts aren't experts in the space of human defined subjects. Their expertise are in some embedding space, which might or might not line up with our intuition of a subject.

by Closi 3y ago

capableweb 3y ago

> It's worth noting that GPT-4 internally uses a Mixture of Experts (MoE) model with 8 'experts' internally Has this actually been confirmed, either officially by OpenAI or otherwise? As far as I know, George Hotz claimed this once, and since then everyone just assumed it was the truth without actually waiting for any sort of verification.

sheepscreek 3y ago
TL;DR This might work - but it will be like watching Groundhog Day. It will require many iterations, make too many mistakes to get there and won’t remember a thing. My naive understanding of layers in a model is that each layer loosely acts as an expert in one step of the entire process. For example, in an object recognition model, one layer takes on the task of separating objects from the background, another excels at knowing the colour of different things, another might learn the difference between a blue sky vs. the colour sky blue. So essentially, we’re trying to mimic the same working model at a higher level of abstraction. Similar to how our body is made of atoms. Many atoms make a molecule. Many molecules make organic tissue, and amino acids that perform more complex operations. Skip all the way up and you have a human being. Human beings in numbers can put a man on the moon, create anti-matter and nuclear explosions. In theory - this can work just as well. One agent to provide a solution, another to critique it, another to verify it, another to mimic the end-user. The big obvious missing piece is memory and the ability to learn while doing (at least in existing LLMs). It’s like having a software development team that’s frozen in time and knowledge. These LLM agents will always require some micromanagement and hand holding, and will waste a lot of resources with failed attempts - that they are going to repeat every time you ask them to perform this task.

zoogeny 3y ago

Right, but the intuition I am going for isn't "Are 10 high-school students who collaborate together smarter than 1 high-school student working on their own". The intuition I am asking for is more like: "Are 10, 100, or 1000 GPT-3s capable of producing the equivalent intelligence of 1 GPT-4". That kind of reasoning expanded to GPT-N. And further, if no number of GPT-3s can reach the level of intelligence of a GPT-4, then what set of problems can be solved by 100s of GPT-3s in a mixed model and what problems require the single GPT-4.

visarga 3y ago
You can generally amplify models by using more tokens, more calls or relying on some oracle of correctness. I see rapid growth of synthetic datasets from now on, we need the diversity of synthetic data to progress further.

lordofgibbons 3y ago

> It's worth noting that GPT-4 internally uses a Mixture of Experts (MoE) model with 8 'experts' internally, so it's more similar to a multi-agent setup than you might think initially. That's a very common misunderstanding of what MoEs are. What you're describing is an Ensemble. A MoE is where a the input is "routed" to one of X "experts" in the higher layers of the network. Something like this: {input} -> [lower layers] -> <decide which upper layers to send input to> -> [expert] -> output And these experts aren't experts in the space of human defined subjects. Their expertise are in some embedding space, which might or might not line up with our intuition of a subject.