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Prompted LLMs as Chatbot Modules for Long Open-domain Conversation

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arxiv 2305.04533 v1 pith:3NSVRUGB submitted 2023-05-08 cs.CL cs.AIcs.LG

classification cs.CLcs.AIcs.LG
keywords chatbotcreatingllmsmodelsmodulesopen-domainpromptedagents
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In this paper, we propose MPC (Modular Prompted Chatbot), a new approach for creating high-quality conversational agents without the need for fine-tuning. Our method utilizes pre-trained large language models (LLMs) as individual modules for long-term consistency and flexibility, by using techniques such as few-shot prompting, chain-of-thought (CoT), and external memory. Our human evaluation results show that MPC is on par with fine-tuned chatbot models in open-domain conversations, making it an effective solution for creating consistent and engaging chatbots.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CAIM: Development and Evaluation of a Cognitive AI Memory Framework for Long-Term Interaction with Intelligent Agents

    cs.AI 2025-05 conditional novelty 6.0 of 10

    CAIM, a cognitive-AI-inspired memory framework with ontology-based tagging and relevance filtering, improves retrieval and response correctness for LLM assistants on the Generated Virtual Dataset compared with MemoryB...

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