Yanfei Chen
Yanfei Chen received his PhD in Industrial Engineering from University of Pittsburgh in 2018. He joined Google as a software engineer in Cloud AI, working on building Anti Money Laundering AI on the Google Cloud Platform. Currently, he is in the Cloud AI Research team, working on various research projects to bring the state of the art AI into Google Cloud for enterprise customers. His research interests are natural language processing, data mining and optimization.
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With development of Large Language Models (LLMs), collaboration between LLMs to solve complex tasks has attracted more and more attention. An important challenging task is reasoning from long text that cannot be input into LLMs. Thus far, limited research has explored how to solve long context tasks via pure multi-agent collaboration.
In this paper, we propose Chain-of-Agents (CoA), a novel framework that leverages the multi-agent collaboration via natural language to solve complex tasks. In CoA, the long text is split into chunks to be processed by agents repeatedly with appending the information from preceding agents. A manager model is finally employed to obtain the final answer utilizing the output of the last agent.
On wide range of datasets for long context question answering, summarization, and code completion and with many LLMs (including PaLM 2, Claude, and Gemini), we show that CoA framework outperforms strong baselines, including the commonly-used retrieval augmented generation (RAG) systems, by a large margin. For instance, text-bison obtains 13.30\% performance gain on NarrativeQA, and 10.22\% on MuSiQue dataset.
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