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

arXiv:2405.05955v1 Announce Type: new
Abstract: 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 transforming a conventional LLM into a synergistic multi-agent …

abstract accuracy agents arxiv capabilities complexity context cs.cl efficiency emergence human human performance language language models large language large language models llms multiple performance planning tasks tool type