A Human-Computer Collaborative Tool for Training a Single Large Language Model Agent into a Network through Few Examples
arxiv(2024)
摘要
The capabilities of a single large language model (LLM) agent for solving a
complex task are limited. Connecting multiple LLM agents to a network can
effectively improve overall performance. However, building an LLM agent network
(LAN) requires a substantial amount of time and effort. In this paper, we
introduce EasyLAN, a human-computer collaborative tool that helps developers
construct LANs. EasyLAN initially generates a LAN containing only one agent
based on the description of the desired task. Subsequently, EasyLAN leverages a
few training examples to update the LAN. For each example, EasyLAN models the
gap between the output and the ground truth and identifies the causes of the
errors. These errors are addressed through carefully designed strategies. Users
can intervene in EasyLAN's workflow or directly modify the LAN. Eventually, the
LAN evolves from a single agent to a network of LLM agents. The experimental
results indicate that developers can rapidly construct LANs with good
performance.
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