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This paper introduces the retrieval-augmented large language model with Definite Finite Automaton (DFA-RAG), a novel framework designed to enhance the capabilities of conversational agents using large language models (LLMs).
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Fantastic rewards and how to tame them: A case study on reward learning for task-oriented dialogue systems
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Semantic router: Enhancing control in llm conversations
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