ChatLLM Network: More brains, More intelligence

TL;DR

The paper introduces ChatLLM, a network of multiple ChatGPT-based models that interact and collaborate to improve problem-solving, stability, and cooperative thinking in dialogue-based AI systems.

Contribution

It proposes a novel multi-agent ChatGPT network with feedback mechanisms for enhanced cooperation and decision-making in dialogue AI.

Findings

Significant improvement in problem-solving accuracy.

Enhanced stability and diversity of responses.

Progress in cooperative thinking among models.

Abstract

Dialogue-based language models mark a huge milestone in the field of artificial intelligence, by their impressive ability to interact with users, as well as a series of challenging tasks prompted by customized instructions. However, the prevalent large-scale dialogue-based language models like ChatGPT still have room for improvement, such as unstable responses to questions and the inability to think cooperatively like humans. Considering the ability of dialogue-based language models in conversation and their inherent randomness in thinking, we propose ChatLLM network that allows multiple dialogue-based language models to interact, provide feedback, and think together. We design the network of ChatLLMs based on ChatGPT. Specifically, individual instances of ChatGPT may possess distinct perspectives towards the same problem, and by consolidating these diverse viewpoints via a separate ChatGPT, the ChatLLM network system can conduct decision-making more objectively and comprehensively. In addition, a language-based feedback mechanism comparable to backpropagation is devised to update the ChatGPTs within the network. Experiments on two datasets demonstrate that our network attains significant improvements in problem-solving, leading to observable progress amongst each member.