A Human-Computer Collaborative Tool for Training a Single Large Language Model Agent into a Network through Few Examples

TL;DR

EasyLAN is a collaborative tool that enables rapid construction of large language model networks by iteratively training and refining agent interactions with minimal examples.

Contribution

The paper introduces EasyLAN, a novel human-computer collaborative system for efficiently building LLM agent networks with few training examples.

Findings

Rapid LAN construction with minimal examples

Effective error modeling and correction strategies

Improved performance of LLM networks

Abstract

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.