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Smurfs: Leveraging Multiple Proficiency Agents with Context-Efficiency for Tool Planning

The emergence of large language models (LLMs) has opened up unprecedented possibilities for automating complex tasks that are often comparable to human performance. Despite their capabilities, LLMs still encounter difficulties in completing tasks that require high levels of accuracy and complexity due to their inherent limitations in handling multifaceted problems single-handedly. This paper introduces `Smurfs', a cutting-edge multi-agent framework designed to revolutionize the application of LLMs. By seamlessly transforming a conventional LLM into a synergistic multi-agent ensemble, Smurfs can enhance the model's ability to solve complex tasks at no additional cost. This is achieved through innovative prompting strategies that allocate distinct roles within the model, thereby facilitating collaboration among specialized agents and forming an intelligent multi-agent system. Our empirical investigation on both open-ended task of StableToolBench and closed-ended task on HotpotQA showcases Smurfs' superior capability in intricate tool utilization scenarios. Notably, Smurfs outmatches all the baseline methods in both experiments, setting new state-of-the-art performance. Furthermore, through comprehensive ablation studies, we dissect the contribution of the core components of the multi-agent framework to its overall efficacy. This not only verifies the effectiveness of the framework, but also sets a route for future exploration of multi-agent LLM systems.

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