  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%.
