This paper introduces RAISE (Reasoning and Acting through Scratchpad and Examples), an advanced architecture enhancing the integration of Large Language Models (LLMs) like GPT-4 into conversational agents. RAISE, an enhancement of the ReAct framework, incorporates a dual-component memory system, mirroring human short-term and long-term memory, to maintain context and continuity in conversations. It entails a comprehensive agent construction scenario, including phases like Conversation Selection, Scene Extraction, CoT Completion, and Scene Augmentation, leading to the LLMs Training phase. This approach appears to enhance agent controllability and adaptability in complex, multi-turn dialogues. Our preliminary evaluations in a real estate sales context suggest that RAISE has some advantages over traditional agents, indicating its potential for broader applications. This work contributes to the AI field by providing a robust framework for developing more context-aware and versatile conversational agents.

翻译：本文提出RAISE（通过草稿与示例进行推理与行动）架构，这是一种增强大型语言模型（如GPT-4）在对话智能体中集成能力的先进框架。作为ReAct框架的改进版本，RAISE构建了模拟人类短时记忆与长时记忆的双组件记忆系统，用以维持对话的上下文连贯性。该架构包含完整的智能体构建流程，涵盖对话筛选、场景提取、思维链补全及场景增强等阶段，最终导向大语言模型训练阶段。该方法能有效提升智能体在复杂多轮对话中的可控性与适应性。我们在房地产销售场景中的初步评估表明，RAISE相较于传统智能体具有显著优势，展现出更广泛的应用潜力。本研究通过构建稳健框架推动了上下文感知型多功能对话智能体的发展，为人工智能领域做出了贡献。