Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence (2410.11163v2)

Abstract: We propose Model Swarms, a collaborative search algorithm to adapt LLMs via swarm intelligence, the collective behavior guiding individual systems. Specifically, Model Swarms starts with a pool of LLM experts and a utility function. Guided by the best-found checkpoints across models, diverse LLM experts collaboratively move in the weight space and optimize a utility function representing model adaptation objectives. Compared to existing model composition approaches, Model Swarms offers tuning-free model adaptation, works in low-data regimes with as few as 200 examples, and does not require assumptions about specific experts in the swarm or how they should be composed. Extensive experiments demonstrate that Model Swarms could flexibly adapt LLM experts to a single task, multi-task domains, reward models, as well as diverse human interests, improving over 12 model composition baselines by up to 21.0% across tasks and contexts. Further analysis reveals that LLM experts discover previously unseen capabilities in initial checkpoints and that Model Swarms enable the weak-to-strong transition of experts through the collaborative search process.

• The paper introduces Model Swarms, a collaborative algorithm inspired by particle swarm optimization to adapt LLM experts without extensive tuning.
• It demonstrates a 13.3% improvement over 12 baselines in reasoning tasks and achieves Pareto-optimal solutions across multi-task domains.
• The approach offers a flexible, data-efficient framework for LLM adaptation with potential extensions to heterogeneous architectures and accelerated convergence.

Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence

The paper introduces "Model Swarms," a novel collaborative search algorithm designed to adapt LLMs using principles of swarm intelligence. The methodology capitalizes on the collective behavior of diverse LLM experts to optimize adaptability in various tasks and domains without extensive tuning data or assumptions about the experts involved.

Methodology

Model Swarms draws inspiration from Particle Swarm Optimization (PSO). Each LLM expert functions as a particle moving in the model weight space, guided by a utility function representing specific adaptation objectives. The algorithm begins with a pool of diverse LLM experts and proceeds by collaboratively optimizing these models guided by checkpoints representing personal and global best utilities.

Key steps in the process include:

1. Initialization: Start with a diverse set of LLM experts and expand this pool through pairwise interpolation, ensuring a variety of starting particles.
2. Velocity and Weight Updates: The velocity of each particle is updated based on inertia, personal best, global best, and worst positions among all particles. This enables exploration in promising neighborhoods by adjusting model weights towards optimal checkpoint configurations.
3. Iterative Search and Convergence: The iterative process concludes once the global best parameters stabilize or reach certain iterations, outputting an adapted model through collaborative search.

Empirical Results

Extensive experiments highlight the superiority of Model Swarms across four adaptation objectives:

• Single Task Adaptation: Model Swarms outperforms 12 model composition baselines by 13.3% on average across several datasets, particularly excelling in reasoning tasks.
• Multi-Task Domains: The approach demonstrates Pareto-optimal solutions, effectively balancing performance across various domains like medical and legal.
• Reward Models: The framework shows considerable improvements in reward model scores, outperforming traditional methods like PPO and DPO, particularly in adapting to contradictory preferences.
• Human Interests: In tasks driven by human interest topics, Model Swarms delivers improved performance both in AI scoring and human evaluations, showcasing its potential in aligning LLMs with diverse user needs.

Theoretical Contributions and Practical Implications

Model Swarms provides a flexible, data-efficient framework for adapting LLMs. It emphasizes the value of diverse expert collaboration without rigid structural assumptions. The successful adaptation to a multitude of objectives, even with minimal data, suggests broad applicability in modular AI systems. Additionally, the emergence of new capabilities suggests potential for discovering novel AI competencies via collaborative optimization.

Future Directions

Future research could explore the extension of Model Swarms to heterogeneous expert compositions across different architectures. Further optimization strategies, such as incorporating accelerated convergence mechanisms, will enhance computational efficiency. Understanding the emergent capabilities and search dynamics through more detailed analyses will refine the adaptation processes even further.

Overall, Model Swarms introduces a pivotal step forward in collaborative AI systems, aligning diverse models towards shared and adaptive objectives, and holds promise for leveraging swarm intelligence in the ongoing evolution of LLM technology.