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Model Swarms
🤝 Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence
This paper presents a new method called MODEL SWARMS, a collaborative search algorithm for adapting large language models (LLMs) using swarm intelligence. The researchers propose viewing each LLM expert as a "particle" in a swarm and use particle swarm optimization (PSO) to collaboratively search the weight space for optimized models. This approach allows LLMs to adapt to a variety of objectives, including single tasks, multi-task domains, reward models, and human interests, without requiring large amounts of training data. Extensive experiments demonstrate that MODEL SWARMS outperforms existing model composition baselines and enables the discovery of previously unseen capabilities in LLMs.
📎 Link to paper
This paper presents a new method called MODEL SWARMS, a collaborative search algorithm for adapting large language models (LLMs) using swarm intelligence. The researchers propose viewing each LLM expert as a "particle" in a swarm and use particle swarm optimization (PSO) to collaboratively search the weight space for optimized models. This approach allows LLMs to adapt to a variety of objectives, including single tasks, multi-task domains, reward models, and human interests, without requiring large amounts of training data. Extensive experiments demonstrate that MODEL SWARMS outperforms existing model composition baselines and enables the discovery of previously unseen capabilities in LLMs.
📎 Link to paper
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By Shahriar Shariati🤝 Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence
This paper presents a new method called MODEL SWARMS, a collaborative search algorithm for adapting large language models (LLMs) using swarm intelligence. The researchers propose viewing each LLM expert as a "particle" in a swarm and use particle swarm optimization (PSO) to collaboratively search the weight space for optimized models. This approach allows LLMs to adapt to a variety of objectives, including single tasks, multi-task domains, reward models, and human interests, without requiring large amounts of training data. Extensive experiments demonstrate that MODEL SWARMS outperforms existing model composition baselines and enables the discovery of previously unseen capabilities in LLMs.
📎 Link to paper
This paper presents a new method called MODEL SWARMS, a collaborative search algorithm for adapting large language models (LLMs) using swarm intelligence. The researchers propose viewing each LLM expert as a "particle" in a swarm and use particle swarm optimization (PSO) to collaboratively search the weight space for optimized models. This approach allows LLMs to adapt to a variety of objectives, including single tasks, multi-task domains, reward models, and human interests, without requiring large amounts of training data. Extensive experiments demonstrate that MODEL SWARMS outperforms existing model composition baselines and enables the discovery of previously unseen capabilities in LLMs.
📎 Link to paper