While the trend of decentralized governance is obvious (cryptocurrencies and blockchains are widely adopted by multiple sovereign countries), initiating governance proposals within Decentralized Autonomous Organizations (DAOs) is still challenging, i.e., it requires providing a low-level transaction payload, therefore posing significant barriers to broad community participation. To address these challenges, we propose a multi-agent system powered by Large Language Models with a novel Label-Centric Retrieval algorithm to automate the translation from natural language inputs into executable proposal transactions. The system incorporates DAOLang, a Domain-Specific Language to simplify the specification of various governance proposals. The key optimization achieved by DAOLang is a semantic-aware abstraction of user input that reliably secures proposal generation with a low level of token demand. A preliminary evaluation on real-world applications reflects the potential of DAOLang in terms of generating complicated types of proposals with existing foundation models, e.g. GPT-4o.

翻译：尽管去中心化治理的趋势日益显著（加密货币和区块链已被多个主权国家广泛采纳），但在去中心化自治组织（DAOs）中发起治理提案仍然面临挑战，即需要提供底层交易负载，这为广泛的社区参与设置了显著障碍。为应对这些挑战，我们提出了一种由大型语言模型驱动的多智能体系统，该系统采用新颖的以标签为中心的检索算法，能够将自然语言输入自动转换为可执行的提案交易。该系统集成了DAOLang——一种用于简化各类治理提案规约的领域特定语言。DAOLang实现的关键优化在于对用户输入进行语义感知抽象，从而以较低的令牌需求可靠地保障提案生成。在真实应用场景中的初步评估表明，DAOLang在使用现有基础模型（如GPT-4o）生成复杂类型提案方面展现出潜力。