Reliable Academic Conference Question Answering: A Study Based on Large Language Model
Zhiwei Huang, Long Jin, Junjie Wang, Mingchen Tu, Yin Hua, Zhiqiang Liu, Jiawei Meng, Huajun Chen, Wen Zhang
The rapid growth of computer science has led to a proliferation of research presented at academic conferences, fostering global scholarly communication. Researchers consistently seek accurate, current information about these events at all stages. This data surge necessitates an intelligent question-answering system to efficiently address researchers' queries and ensure awareness of the latest advancements. The information of conferences is usually published on their official website, organized in a semi-structured way with a lot of text. To address this need, we have developed the ConferenceQA dataset for 7 diverse academic conferences with human annotations. Firstly, we employ a combination of manual and automated methods to organize academic conference data in a semi-structured JSON format. Subsequently, we annotate nearly 100 question-answer pairs for each conference. Each pair is classified into four different dimensions. To ensure the reliability of the data, we manually annotate the source of each answer. In light of recent advancements, Large Language Models (LLMs) have demonstrated impressive performance in various NLP tasks. They have demonstrated impressive capabilities in information-seeking question answering after instruction fine-tuning, and as such, we present our conference QA study based on LLM. Due to hallucination and outdated knowledge of LLMs, we adopt retrieval based methods to enhance LLMs' question-answering abilities. We have proposed a structure-aware retrieval method, specifically designed to leverage inherent structural information during the retrieval process. Empirical validation on the ConferenceQA dataset has demonstrated the effectiveness of this method. The dataset and code are readily accessible on https://github.com/zjukg/ConferenceQA.