MoA BEATS GPT4o With Open-Source Models!! (With Code!)
Summary
TLDRThe 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
🤖 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.
📈 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.
Mindmap
Keywords
💡Large Language Models (LLMs)
💡Mixture of Agents (MOA)
💡Collaborativeness
💡Proposers
💡Aggregators
💡Alpaca Eval 2.0
💡Quen
💡Open-Source Models
💡Layered Process
💡Rate Limiting
💡Benchmarking
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
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