  Community Question Answering (CQA) becomes increasingly prevalent in recent
years. However, there are a large number of answers, which is difficult for
users to select the relevant answers. Therefore, answer selection is a very
significant subtask of CQA. In this paper, we first propose the Question-Answer
cross attention networks (QAN) with pre-trained models for answer selection and
utilize large language model (LLM) to perform answer selection with knowledge
augmentation. Specifically, we apply the BERT model as the encoder layer to do
pre-training for question subjects, question bodies and answers, respectively,
then the cross attention mechanism selects the most relevant answer for
different questions. Experiments show that the QAN model achieves
state-of-the-art performance on two datasets, SemEval2015 and SemEval2017.
Moreover, we use the LLM to generate external knowledge from questions and
correct answers to achieve knowledge augmentation for the answer selection task
by LLM, while optimizing the prompt of LLM in different aspects. The results
show that the introduction of external knowledge can improve the correct answer
selection rate of LLM on datasets SemEval2015 and SemEval2017. Meanwhile, LLM
can also select the correct answer on more questions by optimized prompt.
