From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models

RAISE, an enhancement exhibit high levels of performance in isolated of the ReAct framework, incorporates a tasks, creating an agent that can sustain coherent, dual-component memory system, mirroring context-aware, and purpose-driven conversations human short-term and long-term memory, remains an intricate endeavor. The need for to maintain context and continuity a more sophisticated framework that leverages the in conversations. It entails a comprehensive strengths of LLMs while addressing their limitations agent construction scenario, including in conversational settings has become increasingly phases like Conversation Selection, apparent. Scene Extraction, CoT Completion, and In response to this need, we introduce the Scene Augmentation, leading to the LLMs RAISE (Reasoning and Acting through Scratchpad Training phase. This approach appears to and Examples) architecture. RAISE represents enhance agent controllability and adaptability a refined enhancement of the existing Rein complex, multi-turn dialogues. Act(Yao et al., 2023) framework, specifically designed Our preliminary evaluations in a real estate to augment the capabilities of conversational sales context suggest that RAISE has agents. This paper presents a detailed exploration some advantages over traditional agents, of RAISE, highlighting its unique components indicating its potential for broader applications.