With over 200 million published academic documents and millions of new
documents being written each year, academic researchers face the challenge of
searching for information within this vast corpus. However, existing retrieval
systems struggle to understand the semantics and domain knowledge present in
academic papers. In this work, we demonstrate that by utilizing large language
models, a document retrieval system can achieve advanced semantic understanding
capabilities, significantly outperforming existing systems. Our approach
involves training the retriever and reranker using domain-specific data
generated by large language models. Additionally, we utilize large language
models to identify candidates from the references of retrieved papers to
further enhance the performance. We use a test set annotated by academic
researchers in the fields of quantum physics and computer vision to evaluate
our system's performance. The results show that DocReLM achieves a Top 10
accuracy of 44.12% in computer vision, compared to Google Scholar's 15.69%, and
an increase to 36.21% in quantum physics, while that of Google Scholar is
12.96%.

通过使用大型语言模型，我们展示了文献检索系统能够达到先进的语义理解能力，显著优于现有系统；我们的方法包括使用大型语言模型生成的领域特定数据来训练检索器和再排名器，此外，我们还利用大型语言模型从检索到的论文参考文献中识别候选项以进一步提高性能；我们使用量子物理学和计算机视觉领域的学术研究人员标注的测试集来评估系统的性能，结果显示 DocReLM 在计算机视觉领域的前十准确率为 44.12%，而 Google Scholar 为 15.69%，在量子物理学中提高到 36.21%，而 Google Scholar 为 12.96%。