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.

翻译：基于对话的语言模型通过其与用户交互的卓越能力，以及在定制指令驱动下完成一系列具有挑战性的任务，标志着人工智能领域的重大里程碑。然而，当前主流的大规模对话语言模型（如ChatGPT）仍存在改进空间，例如对问题的回答不稳定，以及无法像人类一样进行协作性思考。考虑到对话语言模型在对话中的能力以及其固有的思维随机性，我们提出ChatLLM网络，该网络允许多个对话语言模型进行交互、反馈与协同思考。我们基于ChatGPT设计了ChatLLM网络。具体而言，单个ChatGPT实例对同一问题可能存在不同视角，通过独立的ChatGPT整合这些多样化观点，ChatLLM网络系统能够更客观、全面地做出决策。此外，我们还设计了一种类似于反向传播的基于语言的反馈机制，用于更新网络中的ChatGPT实例。在两个数据集上的实验表明，我们的网络在问题解决方面取得了显著改进，并促进了各成员之间的可观测进步。