Large language models (LLMs) , trained on vast datasets and aligned with human preferences, are incredibly capable. However, they still face limitations related to their size and the amount of training data they need to function. Scaling up these models is expensive and requires extensive re-training for adding any new knowledge. Interestingly, different LLMs excel in various aspects of language tasks. Some might be better at following complex instructions, while others shine at code generation. This diversity in strengths leads to an intriguing question: Can we harness the collective expertise of multiple LLMs to create a model that surpasses the capabilities of any individual model?
This paper introduces the "Mixture-of-Agents" (MoA) methodology, which leverages the collaborative nature of LLMs to iteratively improve response quality. This collaborative nature is demonstrated by the paper's finding that LLMs tend to produce better responses when presented with outputs from other models, even if those outputs are less capable by themselves. This is the core idea behind MoA.
Think of MoA like a chain of experts. You start with a prompt, and several LLMs independently generate responses. These responses are then fed to another set of LLMs, which might be the same models or different ones, and this process repeats over several "layers." Each layer acts like a team of experts discussing and refining the responses, ultimately leading to a more robust and comprehensive final output.
The paper proposes the Mixture-of-Agents (MoA) methodology to harness the collaborative potential of LLMs and iteratively refine responses. Let's dive into each section of the methodology:
The paper emphasizes the "collaborativeness" of LLMs, arguing that LLMs tend to generate higher-quality responses when presented with outputs from other models. This observation, backed by the empirical evidence shown in Figure 1, forms the cornerstone of the MoA approach.
The paper categorizes LLMs into two roles:
Proposers
Models that excel at generating useful reference responses for other models to build upon. Proposers might not produce the highest-scoring answers themselves, but they offer diverse perspectives and context, contributing to a richer final output.
Aggregators
Models adept at synthesizing responses from other models into a single, high-quality output. Effective aggregators enhance the quality of the final response, even when working with inputs that are less capable than their own. Many LLMs can act as both proposers and aggregators, while others demonstrate specialized proficiencies. For instance, GPT-4o, Qwen1.5, and LLaMA-3 are versatile, effective in both roles, while WizardLM excels as a proposer but struggles with aggregation.
MoA is a layered architecture where each layer comprises multiple LLMs, called "agents." Each agent takes the outputs from the previous layer as auxiliary information to generate its response. This iterative process continues over several layers, each layer refining the response based on the insights from previous layers.
The MoA approach is designed to be flexible and scalable, using only the prompting interface of LLMs without requiring any fine-tuning. This flexibility enables the inclusion of new LLMs as they become available, making the approach adaptable to the rapidly evolving landscape of LLM technology.
The paper draws inspiration from the concept of "Mixture-of-Experts" (MoE) in machine learning. MoE architectures use multiple expert networks, each specializing in different aspects of a task. However, MoA operates at the model level, using multiple LLMs as the experts, rather than specialized sub-networks within a single model as in MoE.
MoA's key advantage over MoE lies in its simplicity and flexibility. It leverages the inherent prompting capabilities of LLMs, eliminating the need for fine-tuning or modifying internal activations or weights. This approach simplifies implementation and allows for easy integration of new LLMs.
The paper evaluates the effectiveness of MoA through comprehensive experiments across three prominent benchmarks: AlpacaEval 2.0, MT-Bench, and FLASK.
AlpacaEval 2.0: This benchmark is designed to assess the alignment of LLMs with human preferences, focusing on real-world instruction following.
MT-Bench: This benchmark evaluates model performance based on a GPT-4-based scoring system.
FLASK: FLASK offers a more granular evaluation, providing 12 skill-specific scores for each model, allowing for a deeper understanding of model strengths and weaknesses across various language tasks.
The paper uses several open-source models, including:Qwen1.5-110B-Chat, Qwen1.5-72B-Chat, WizardLM-8x22B, LLaMA-3-70B-Instruct, Mixtral-8x22B-v0.1, dbrx-instruct
These models are combined into different MoA configurations, such as the "default" MoA using only open-source models, "MoA w/ GPT-4o" using GPT-4o as the aggregator in the final layer, and "MoA-Lite" using a smaller number of layers for cost-effectiveness.
MoA significantly outperforms leading models like GPT-4 and other state-of-the-art open-source models on AlpacaEval 2.0, achieving a remarkable 8.2% improvement over GPT-4o. Notably, MoA outperforms GPT-4o using solely open-source models, highlighting its effectiveness in leveraging the capabilities of these models. MoA-Lite, with its simpler and more cost-effective architecture, still outperforms GPT-4o by 1.8%, showcasing the adaptability of the method across different budget constraints.
While the improvements over individual models on MT-Bench are relatively incremental, it's important to consider that the current models already perform remarkably well on this benchmark. Even with these marginal enhancements, MoA secures the top position on the leaderboard, demonstrating its ability to further optimize even highly optimized benchmarks.
FLASK offers a fine-grained evaluation, revealing that MoA excels in various aspects, including robustness, correctness, efficiency, factuality, commonsense, insightfulness, completeness, and metacognition. It outperforms GPT-4 Omni in terms of correctness, factuality, insightfulness, completeness, and metacognition, indicating its strength in various reasoning and language understanding tasks. MoA, however, struggles with conciseness, producing outputs that are slightly more verbose.
Cost Effectiveness
The plot of the LC win rate against the average inference cost, reveals a Pareto front where certain models strike a balance between cost and performance. MoA lies closer to this Pareto front, offering high performance at a lower cost compared to single models like GPT-4 Turbo and GPT-4o. MoA-Lite even matches GPT-4o's cost while achieving a higher quality, demonstrating its cost-effectiveness.
Tflops Consumption
The paper explores the relationship between LC win rate and the number of teraflops (tflops), a proxy for latency. Again, MoA shows its efficiency, lying on the Pareto front, effectively utilizing its computational resources to maximize performance.
The paper goes into the internal mechanism of MoA to understand the key factors contributing to its effectiveness.
MoA outperforms an LLM-ranker baseline, suggesting that the aggregator doesn't simply select one of the proposed answers but performs sophisticated aggregation over all generated outputs. This highlights the importance of the aggregator's role in combining and refining the diverse perspectives from the proposers.
The paper shows a positive correlation between the BLEU score, a measure of n-gram overlap, and the win rate of the proposed outputs. This suggests that the aggregator tends to incorporate the best elements from the proposed answers, contributing to the overall quality of the final output.
The paper demonstrates that using a diverse set of LLMs as proposers and increasing the number of proposers in each layer leads to consistent performance improvements. This underlines the importance of diverse perspectives and the benefits of having a wider range of inputs for the aggregation process.
The paper also investigates the specialization of models within the MoA ecosystem. It identifies GPT-4o, Qwen1.5, and LLaMA-3 as versatile models effective in both proposing and aggregating responses. In contrast, WizardLM excels as a proposer but struggles with aggregation, highlighting the potential for models to specialize in specific roles within the MoA framework.
MoA's ability to outperform single models on benchmarks like AlpacaEval 2.0, MT-Bench, and FLASK suggests a significant leap in accuracy and quality of LLM-generated outputs. This translates to more reliable and accurate responses in applications like customer service chatbots, content creation tools, and code generation platforms.
The paper emphasizes the interpretability of MoA, as the intermediate outputs are expressed in natural language. This transparency into the reasoning process enables developers to understand and debug the model's decision-making, leading to more robust and reliable solutions.
MoA opens up exciting possibilities for businesses to develop advanced LLM applications previously restricted due to limitations in existing models. For example, imagine a legal assistant chatbot that leverages MoA to synthesize information from multiple legal databases, providing accurate and comprehensive advice.
MoA's flexibility in integrating new LLMs allows for continuous improvement and adaptation to evolving requirements. As new models with specialized capabilities emerge, businesses can easily incorporate them into their MoA architecture, ensuring continuous improvement and enhanced performance.
The paper concludes by highlighting the significance of MoA in harnessing the collective strengths of multiple LLMs. The proposed method showcases its effectiveness in improving response quality, outperforming even GPT-4 Omni on several benchmarks. The paper also acknowledges limitations, such as the potential for high time-to-first-token (TTFT), and suggests future work to address these issues.
The paper emphasizes the broader impact of MoA, arguing that it has the potential to make AI more accessible and improve the interpretability of LLMs. This enhanced interpretability facilitates a deeper understanding of the model's reasoning process, leading to greater trust and confidence in AI-powered solutions.
While the paper presents a compelling case for the effectiveness of Mixture-of-Agents (MoA), there are certain aspects that could be explored further
While the paper establishes the phenomenon of "collaborativeness" in LLMs, it is defined in a very narrow manner. Could do well with deeper understanding the underlying mechanisms. What specific linguistic or reasoning patterns contribute to this improvement in response quality? Identifying these patterns could lead to more tailored strategies for designing MoA architectures.
The part about certain models being naturally good "proposers" or "aggregators" is empirical - may hold in some cases, but no methodology is provided. While the experiments provide some evidence for this, further investigation into model specialization could be beneficial. Are there specific characteristics of a model that make it a better proposer or aggregator?
The paper emphasizes the "no fine-tuning" approach of MoA. However, could there be benefits to training models specifically for the MoA framework? This could potentially lead to more efficient and effective aggregation, especially for specific tasks or domains.
The paper explores the benefits of using multiple proposers but doesn't fully explore the "single proposer" setting, where the same model is used to generate multiple responses. This setting could be beneficial for situations where computational resources are limited. A more comprehensive analysis of this setting could provide valuable insights for practical applications.
The paper primarily focuses on instruction following and question-answering tasks. How does MoA perform on more complex tasks, such as code generation, creative writing, or multi-step reasoning? Exploring the applicability of MoA across a broader range of tasks would strengthen its practical relevance.
While the paper demonstrates the effectiveness of MoA compared to LLM rankers, a more detailed analysis of the aggregation process itself could be beneficial. What exactly is the aggregator "doing" to improve the output? Understanding the inner workings of this process could lead to further refinements in the design of MoA.
Overall, the paper presents a promising and innovative approach to enhancing LLM capabilities. However, further research into the intricacies of "collaborativeness", model specialization, training strategies, and the applicability of MoA across diverse tasks would provide a more comprehensive understanding of its strengths and limitations. This would pave the way for a more robust and practical application of MoA in real-world scenarios.