  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).
Traditional LLMs face challenges in generating regulated and compliant
responses in special scenarios with predetermined response guidelines, like
emotional support and customer service. Our framework addresses these
challenges by embedding a Definite Finite Automaton (DFA), learned from
training dialogues, within the LLM. This structured approach acts as a semantic
router which enables the LLM to adhere to a deterministic response pathway. The
routing is achieved by the retrieval-augmentation generation (RAG) strategy,
which carefully selects dialogue examples aligned with the current
conversational context. The advantages of DFA-RAG include an interpretable
structure through human-readable DFA, context-aware retrieval for responses in
conversations, and plug-and-play compatibility with existing LLMs. Extensive
benchmarks validate DFA-RAG's effectiveness, indicating its potential as a
valuable contribution to the conversational agent.
