提出RACI框架，让人类与大模型团队协作更可信、分工更清晰。
Facilitating Trustworthy Human-Agent Collaboration in LLM-based Multi-Agent System oriented Software Engineering
- 基于RACI模型设计人机任务分配机制，明确责任边界。
- 提升协作效率，确保可问责性，降低大模型自动化风险。
- 适合关注AI可信协作的软件工程研究者与实践者。
多智能体自主系统（MAS）在应对跨领域挑战方面优于单一智能体，这一优势同样适用于软件工程领域。当前主流研究将大语言模型（LLM）嵌入智能体核心，构建基于大模型的多智能体系统（LMA）。然而，将LMA系统引入软件工程带来了诸多挑战，其中最主要的是如何以可信赖的方式在人类与系统间合理分配任务。本文提出一种基于RACI的框架，包含实施指南及实例实现，可促进高效协作，保障责任可追溯，并缓解大模型驱动自动化带来的潜在风险，同时符合可信AI准则。文中还规划了未来拟开展的实证验证方法。
原文摘要 · Abstract (English)
Multi-agent autonomous systems (MAS) are better at addressing challenges that spans across multiple domains than singular autonomous agents. This holds true within the field of software engineering (SE) as well. The state-of-the-art research on MAS within SE focuses on integrating LLMs at the core of autonomous agents to create LLM-based multi-agent autonomous (LMA) systems. However, the introduction of LMA systems into SE brings a plethora of challenges. One of the major challenges is the strategic allocation of tasks between humans and the LMA system in a trustworthy manner. To address this challenge, a RACI-based framework is proposed in this work in progress article, along with implementation guidelines and an example implementation of the framework. The proposed framework can facilitate efficient collaboration, ensure accountability, and mitigate potential risks associated with LLM-driven automation while aligning with the Trustworthy AI guidelines. The future steps for this work delineating the planned empirical validation method are also presented.
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