Chatbot Meets Pipeline: Augment Large Language Model with Definite
  Finite Automaton
Yiyou Sun Junjie Hu Wei Cheng Haifeng Chen
This paper introduces the Definite Finite Automaton augmented large language
model (DFA-LLM), 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 enables the LLM to adhere to a deterministic response
pathway, guided by the DFA. The advantages of DFA-LLM 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-LLM's effectiveness, indicating its potential as a
valuable contribution to the conversational agent.