From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models
CoRR(2024)
摘要
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.
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