MoA BEATS GPT4o With Open-Source Models!! (With Code!)

Summary

The video discusses a breakthrough in AI research where multiple large language models (LLMs) collaborate as 'mixture of agents' (MOA) to outperform the leading model, GPT-40. The research, published by Together AI, demonstrates that by leveraging the collective strengths of various open-source models, the MOA achieves higher accuracy on the ALPACA eval 2.0 benchmark. The video also explores the collaborative architecture of MOA, which consists of layers with different agents working together to refine responses. The presenter tests the MOA with a prompt and shares the successful result, suggesting the potential of this approach for future AI development.

Takeaways

• 📄 The script discusses a new research paper on 'Mixture of Agents' (MOA), which is a collective intelligence approach using multiple large language models (LLMs) to surpass the capabilities of a single model like GPT-40.
• 🤖 The concept of 'collaborativeness' among LLMs is highlighted, where models generate better responses when considering outputs from other models, even if those are less capable individually.
• 🔍 The paper introduces a layered architecture for MOA, with each layer consisting of three agents that refine the output from the previous layer, leading to a more robust and versatile final response.
• 🏆 Together AI's MOA achieved a score of 65.1 on Alpaca Eval 2.0, significantly surpassing the previous leader GPT-40, which scored 57.5.
• 💡 The research demonstrates that using a combination of open-source models as proposers and a large model as an aggregator can yield high-quality responses.
• 🔧 The script mentions the trade-off of higher accuracy in MOA at the cost of slower time to the first token, suggesting that reducing latency is a future research direction.
• 🔄 The process of collaboration involves categorizing models into 'proposers' that generate initial responses and 'aggregators' that synthesize these into a refined output.
• 📈 Experiments show that the performance of MOA consistently improves with each additional layer and that multiple proposers enhance the output quality.
• 👥 The value of diverse perspectives is emphasized, drawing a parallel to human collaboration where a variety of opinions can lead to better outcomes.
• 🛠️ The script includes a live demo of using MOA with different LLMs, showcasing the practical application and effectiveness of the approach.
• 📚 The code for Together MOA is open-source, allowing others to view, learn from, and potentially contribute to the project.

Q & A

What is the main topic discussed in the video script?

The main topic discussed is the concept of 'Mixture of Agents' (MOA), a collective intelligence approach using multiple large language models (LLMs) to improve output quality beyond that of a single model like GPT-40.

What is the significance of the research paper published by Together AI on June 11th?

The research paper introduces the MOA approach, demonstrating that a collaborative system of LLMs can achieve higher scores on the ALPACA eval 2.0 benchmark, surpassing the performance of GPT-40.

What does the acronym 'MOA' stand for in the context of the video script?

MOA stands for 'Mixture of Agents,' which refers to the integration of multiple open-source LLMs to enhance the capabilities of AI systems.

How does the MOA approach differ from using a single generalist LLM like GPT-40?

MOA differs by leveraging the strengths of multiple specialized LLMs working together, which can be more efficient, cost-effective, and potentially performant as a generalist model like GPT-40.

What is the role of 'proposers' in the MOA system?

Proposers are models within the MOA system that generate initial reference responses, offering diverse perspectives that serve as valuable references for the aggregators.

What function do 'aggregators' serve in the MOA architecture?

Aggregators synthesize the different responses from proposers into a single high-quality response, improving the overall output by integrating various insights.

What is the significance of the layered process in the MOA system?

The layered process allows for an iterative improvement of responses, with each layer enhancing the output based on the inputs from the previous layer, leading to a more robust and comprehensive final response.

How does the number of proposers impact the performance of the MOA system?

The performance of the MOA system consistently improves with an increase in the number of proposers, indicating that a wider variety of inputs from different models significantly enhances the output quality.

What is the trade-off when using the MOA system compared to a single model like GPT-40?

While MOA achieves higher accuracy, it does so at the cost of a slower time to the first token, increasing latency, which is identified as a potential area for future research.

What is the potential application of the MOA system demonstrated in the video script?

The video script demonstrates the potential application of the MOA system by testing it with a prompt to generate sentences ending in the word 'apples,' showcasing its ability to produce creative and accurate responses.

What is the viewer's role in the final part of the video script?

The viewer is encouraged to provide feedback on the video, suggest whether a tutorial on using the MOA system's code would be of interest, and to like, subscribe, and comment for further engagement.

Outlines

00:00

🤖 Introduction to Mixture of Agents (MOA) Research

The video script introduces a research paper published by AI on June 11th about 'Together MOA', which stands for Mixture of Agents. The paper discusses a new approach that leverages the collective intelligence of open-source models to surpass the capabilities of the leading generalist model, GPT-40. The concept involves multiple large language models (LLMs) working together in an agentic framework, where each model performs the task it excels at, leading to efficient and cost-effective results. The paper claims that MOA achieves a higher score on the alpaca eval 2.0 benchmark compared to GPT-40, although it comes with a trade-off of slower time to the first token. The script also mentions the potential of integrating GRO (Generative Radix Optimisation) to improve inference times. The architecture of MOA is explained, highlighting a layered approach with agents collaborating at each level to refine responses. The video promises to test the MOA approach and show the results.

05:00

📈 Understanding the Collaboration and Performance of MOA

This paragraph delves deeper into the methodology and findings of the MOA research. It describes the categorization of roles within the MOA framework, with 'proposers' generating initial responses and 'aggregators' synthesizing these into higher-quality outputs. The script explains the layered process, where responses from proposers are iteratively refined by aggregators across multiple layers. The use of six open-source models as proposers and a 110b chat model as the final aggregator is highlighted. The research also investigates the necessity of multiple layers and the impact of the number of proposers on performance, demonstrating the consistent advantage of having more diverse inputs. The script concludes with a live demonstration of the MOA setup, using several reference models, and despite initial rate-limiting errors, successfully generates a response that adheres to the prompt of creating sentences ending with the word 'apples'. The video ends with a call to action for feedback on the methodology and an invitation for viewers to like, subscribe, and comment.

Keywords

💡Large Language Models (LLMs)

Large Language Models (LLMs) refer to artificial intelligence systems that are trained on vast amounts of text data and can generate human-like responses. In the context of the video, LLMs are the core technology behind the research discussed, which involves using multiple LLMs to work collaboratively. The script mentions that allowing these models to take on roles and work together can produce superior outputs, as seen in the 'mixture of agents' approach.

💡Mixture of Agents (MOA)

Mixture of Agents (MOA) is a term introduced in the video to describe a novel approach where multiple LLMs work together to improve the quality of their outputs. The research paper mentioned in the script outlines this approach, which leverages the collective strengths of various open-source models to surpass the capabilities of a single, leading model like GPT-4. The script provides an example of how MOA uses six open-source models as proposers and a final aggregator to refine responses.

💡Collaborativeness

Collaborativeness, in the context of the video, refers to the phenomenon where an LLM generates better responses when it is presented with outputs from other models. This concept is central to the MOA approach, as it highlights the benefits of integrating diverse perspectives from different models. The script illustrates this with examples from the research paper, showing that each model's score significantly increases when leveraging responses from other models.

💡Proposers

In the MOA framework, proposers are models that generate initial reference responses. While a proposer might produce a high-quality response on its own, its main value in this context is to offer diverse perspectives that serve as valuable references for the aggregators. The script mentions that MOA uses six open-source models as proposers, emphasizing the importance of their role in the collaborative process.

💡Aggregators

Aggregators are models within the MOA framework that synthesize the responses from proposers into a single, high-quality response. They play a crucial role in refining the outputs by combining the diverse inputs from various proposers. The script explains that the aggregators are sophisticated in their synthesis process, rather than simply selecting the best response from the reference responses.

💡Alpaca Eval 2.0

Alpaca Eval 2.0 is a benchmarking tool or metric mentioned in the script, used to evaluate the performance of LLMs. The MOA approach is said to achieve a score of 65.1 on this benchmark, surpassing the previous leader, GPT-40, which scored 57.5. This score indicates the effectiveness of the collaborative approach in improving the quality of responses from LLMs.

💡Quen

Quen is a specific LLM mentioned multiple times in the script, used both as a proposer and as the final aggregator in the MOA approach. It is highlighted as a model that, when working in collaboration with other models, can produce high-quality responses. The script also mentions different versions of Quen, such as Quen 272b and Quen 1.5, which are part of the MOA's open-source model collaboration.

💡Open-Source Models

Open-source models refer to AI models whose underlying code is publicly available, allowing anyone to use, modify, and contribute to their development. The script discusses the use of several open-source models in the MOA approach, emphasizing the benefits of leveraging a community-driven development process to improve the collective intelligence of LLMs.

💡Layered Process

The layered process is a method described in the script for improving responses through iterative collaboration among LLMs. It involves multiple layers where proposers generate responses that are then synthesized by aggregators in subsequent layers. This process continues until a more robust and comprehensive response is achieved, as demonstrated in the MOA approach.

💡Rate Limiting

Rate limiting is a technique used to control the amount of requests a system can handle from a single source within a certain period. In the script, the presenter encounters rate limiting errors while querying the models, which is a common issue in systems that manage access to prevent overload. The script notes that the system handled the rate limiting well by retrying the requests.

💡Benchmarking

Benchmarking in the context of the video refers to the process of testing and comparing the performance of different LLMs or approaches, such as MOA, against a standard or other models. The script discusses benchmarking the LC win rate of each layer in MOA and the influence of the number of proposers on performance, which helps in understanding the effectiveness of the collaborative approach.

Highlights

AI research paper introduces 'mixture of agents' (MOA), a new approach to harness the collective strengths of multiple large language models (LLMs).

MOA outperforms GPT-40 on the alpaca eval 2.0 benchmark, achieving a higher score of 65.1 compared to GPT-40's 57.5.

The research demonstrates the power of agentic frameworks, where LLMs take on roles and collaborate to produce the best output.

MOA is more efficient and cost-effective than generalist Frontier Models like GPT-40, and it's open-source.

The basic architecture of MOA consists of multiple layers with three agents each, working in collaboration to refine responses.

The agents in MOA can share the same model or use different models, enhancing the diversity of outputs.

MOA's approach allows for the integration of diverse capabilities and insights from various models, resulting in a robust and versatile combined model.

The research identifies a phenomenon called 'collaborativeness of LLMs', where models generate better responses when presented with outputs from other models.

The paper shows that even models with lower individual capabilities can significantly improve their scores when leveraging responses from other models.

MOA categorizes models into 'proposers' that generate initial responses and 'aggregators' that synthesize these into higher quality responses.

The layered process of MOA involves several proposers generating responses, which are then synthesized by aggregators in subsequent layers.

MOA uses six open-source models as proposers and Quen 1.5 110b chat as the final aggregators.

Experiments show a consistent performance gain with each additional layer in MOA, suggesting the value of multi-layered collaboration.

The number of proposers impacts performance, with more proposers leading to a significant enhancement in output quality.

The research highlights the importance of diverse perspectives and capabilities from different models in improving collaborative AI outcomes.

A live demo of MOA was conducted, showcasing its ability to generate sentences ending with the word 'apples', a task that often challenges models.

Despite rate limiting errors during the demo, MOA successfully generated the desired output, demonstrating robust error handling and functionality.

The video suggests running further benchmarks using MOA's methodology, indicating its potential for future AI development and applications.

Transcripts

give me 10 sentences that end in the

word Apple something that almost all

models struggle with and look at that

final answer from quen and it got it

right really really cool what happens

when you allow multiple large language

models to work together as agents to

produce the best possible output well it

turns out it's actually better than GPT

40 the leading Frontier Model together

AI just published a research paper

outlining what they are calling mixture

of a agents not mixture of experts

mixture of agents and I'm going to tell

you all about it right now and stick

around to the end because I'm actually

going to test it out and I'm going to

show you the results so here's the

research paper published June 11th

together MOA mixture of Agents

collective intelligence of open-source

models pushing the frontier of llm

capabilities now I've been saying for a

while first of all agentic Frameworks

are incredibly powerful when you allow

large language models to take on roles

to have tools and to work together to

produce the best output it tends to be

the best output and especially when you

allow specific large language models to

do the task that they are best at and

you have a bunch of verticalized large

language models working together that

can actually be just as performant as

the generalist Frontier Model like GPT

40 and it's much more efficient it's

much lower cost and it's open source

mixture of a agents an approach to

harness the collective strengths of

multiple llms to improve

state-of-the-art quality we provide

reference implementation together MOA

which leverages several open- Source llm

agents to achieve a score of 65.1 on

alpaca eval 2.0 surpassing prior leader

GPT 40 and not by a little bit 57.5

compared to

65.1 so a substantial win and the cool

thing they published the code so if you

want to see me do a tutorial actually

using this code and it's still kind of

flying under the radar only 144 Stars

let me know in the comments below I'm

happy to do that all right so let's keep

reading so this is the basic

architecture of mixture of agents and

basically what we're seeing here is

multiple layers where each layer has

three different agents working together

in collaboration to come up with the

final output for this prompt and what is

interesting is this has three layers 1 2

3 and each of them as I mentioned has

three agents now you can obviously scale

this up as you see fit and in this

example the agents here can share the

same model which I find to be really

interesting and of course you can use

different models at each layer or for

each agent these agents take outputs

from the previous layer as auxilary

information to generate refined

responses this approach allows MOA

mixture of agents to affect effectively

integrate diverse capabilities and

insights from various models resulting

in a more robust and versatile combined

model so it significantly passes GPT 40

on alpaca eval 2.0 but here's the caveat

while together MOA achieves higher

accuracy it does come at the cost of a

slower time to First token reducing this

latency is an exciting future direction

for This research now you're probably

thinking exactly what I'm thinking Gro

GQ Gro the inference time the time toer

token is insane using Gro so what if we

plugged in Gro to this well that might

be for another video all right so

mixture of Agents our research is based

on a key observation we term the

collaborativeness of llms the phenomenon

where an llm tends to generate better

responses when presented with outputs

from other models even if these other

models are less capable on their own

yeah I've been saying this for a while

this is exactly why agents are so

powerful when different models work

together they produce much better

outputs to investigate if this

phenomenon is prevalent across open

source models we evaluated the score

when leveraging responses from other

models and an answer figure 2 shows that

each model increases significantly from

their base score on alpaca eval 2.0 this

Improvement occurs even when the

reference response quality is lower than

the model's own so here is the example

in yellow for all of these we have an

example where it's just prompt and

response and then in blue much better we

have generate a few different options

and then choose the best option and

here's how they actually set it up to

effectively Leverage The collaboration

of multiple llms we categorize their

roles based on their strengths and

different aspects of collaboration we

have proposers these models generate

initial reference responses while a

proposer might produce a highquality

response on its own its main value lies

in offering nuanced and diverse

perspectives that serve as valuable

references for the aggregator then we

have the aggregators these models

synthesize the different responses from

the proposers into a single highquality

response then based on this

categorization we propose a layered

process to improve responses as

Illustrated in figure one which is what

we're seeing here initially several

proposers independently generate

responses to a given prompt these

responses are then presented to

aggregators in the next layer who

synthesize them into higher quality

responses this iterative process

continues through layers until a more

robust and comprehensive response is

achieved very cool so together Mo MOA

uses six open source models as proposers

and quen 1.5 110b chat as the final

aggregators the six open source models

are wizard LM quen a few different quen

models llama 3 mixol and dbrx so really

taking the best of the open source

models and kind of allowing them to

collaborate with each other which is a

brilliant approach so then they asked

the question do we actually need

multiple layers in MO MOA we also

Benchmark the LC win rate of each layer

of together MOA on alpaca eval 2.0 a

consistent and monotonic performance

gain can be achieved after each layer

all the curves use the same six proposer

agents the only difference is the choice

of the aggregator on top of them we also

added a baseline where a llm ranker

which they're using Quin 1.5 is used to

pick the best response from the

reference responses this further

demonstrates that the aggregator is

sophisticatedly synthesizing rather than

just picking and selecting so after one

layer we can see the performance here

and we can see the increased performance

that tends to flatten out at layer four

that's why they chose three layers next

do we need multiple llms as proposers to

assess the influence of the number of

proposers on performance we conducted

experiments with varying numbers of

proposed answers we can see there is

clearly a consistent Advantage brought

by having more proposer outputs even

with single proposer however the

multiple proposer configuration

consistently outperforms single proposer

indicating that integrating a wider

variety of inputs from different models

significantly enhances the output this

highlights the value of leveraging

diverse perspectives and capabilities

that different models offer this sure

sounds like how humans work together if

you have a bunch of people working

together with very different opinions

that's when you really get magic of

human collaboration all right so I got

it all installed and here we go we're

going to test it out this demo uses the

following llms as reference models we're

powering all of this through together AI

I signed up for an account they are not

sponsoring this video so it is using

quen 272b quen 1.5 72b mixol 8X 22 and

dbrx instruct so what main model do you

want to use and we'll just hit enter for

the default what temperature hit enter

Max tokens fine now let's do our prompt

give me 10 sentences that end in the

word apples something that almost all

models struggle with okay quering all

the models and it looks like I'm getting

rate limited errors but here we go it's

actually still working and look at that

final answer from quen and it got it

right really really cool okay so it

looks like I just got some rate limits

but that's not a big deal it just

retried and worked perfectly so good

airor handling there and yeah this

worked really well actually I I think I

should run my entire Benchmark using

this methodology what do you think let

me know in the comments below if you

liked this video please consider giving

a like And subscribe and I'll see you in

the next one