  Generative Artificial Intelligence (AI), because of its emergent abilities,
has empowered various fields, one typical of which is large language models
(LLMs). One of the typical application fields of Generative AI is large
language models (LLMs), and the natural language understanding capability of
LLM is dramatically improved when compared with conventional AI-based methods.
The natural language understanding capability has always been a barrier to the
intent recognition performance of the Knowledge-Based-Question-and-Answer
(KBQA) system, which arises from linguistic diversity and the newly appeared
intent. Conventional AI-based methods for intent recognition can be divided
into semantic parsing-based and model-based approaches. However, both of the
methods suffer from limited resources in intent recognition. To address this
issue, we propose a novel KBQA system based on a Large Language Model(LLM) and
BERT (LB-KBQA). With the help of generative AI, our proposed method could
detect newly appeared intent and acquire new knowledge. In experiments on
financial domain question answering, our model has demonstrated superior
effectiveness.
