ChatLLM network: More brains, more intelligence

Rui Hao , Linmei Hu , Weijian Qi , Qingliu Wu , Yirui Zhang , Liqiang Nie
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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 a network of ChatLLMs, consisting multiple layers of language models. Specifically, individual instances of language model may possess distinct perspectives towards the same problem, and by consolidating these diverse viewpoints via a separate language model, 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 outputs of the language models within the network. This stratified system of interaction can be analogized to the relationship between leaders and employees in a social organization, where collective decision-making often yields superior judgments or resolutions. Experiments on datasets demonstrate that our network attains significant improvements in problem-solving, leading to observable progress amongst each member.

Abstract Image

基于对话的语言模型在人工智能领域是一个巨大的里程碑,它们与用户互动的能力令人印象深刻,还能根据定制指令完成一系列具有挑战性的任务。然而,目前流行的大型对话式语言模型(如 ChatGPT)仍有改进的余地,如对问题的回答不稳定,无法像人类一样合作思考等。考虑到基于对话的语言模型在对话中的能力及其固有的思维随机性,我们提出了 ChatLLM 网络,它允许多个基于对话的语言模型进行互动、反馈和共同思考。我们设计了一个由多层语言模型组成的 ChatLLM 网络。具体来说,语言模型的各个实例可能对同一问题持有不同的观点,而通过单独的语言模型整合这些不同的观点,ChatLLM 网络系统可以更客观、更全面地进行决策。此外,还设计了一种类似于反向传播的基于语言的反馈机制,用于更新网络内语言模型的输出。这种分层互动系统可以类比为社会组织中领导与员工之间的关系,在这种关系中,集体决策往往会产生更优越的判断或解决方案。数据集上的实验表明,我们的网络在解决问题方面取得了显著的进步,每个成员都取得了可观的进步。
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CiteScore
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