Enhancing Answer Selection in Community Question Answering with Pre-trained and Large Language Models

TL;DR

This paper introduces a novel answer selection method in Community Question Answering using pre-trained BERT models and large language models (LLMs) for knowledge augmentation, achieving state-of-the-art results.

Contribution

It proposes the Question-Answer cross attention networks (QAN) with pre-trained models and leverages LLMs for external knowledge augmentation to improve answer selection accuracy.

Findings

QAN achieves state-of-the-art performance on SemEval datasets.

Knowledge augmentation with LLM improves answer selection accuracy.

Optimized prompts enhance LLM's ability to select correct answers.

Abstract

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.