ScholarCopilot: Training Large Language Models for Academic Writing with
  Accurate Citations
Authors: name: Yubo Wang, name: Xueguang Ma, name: Ping Nie, name: Huaye Zeng, name: Zhiheng Lyu, name: Yuxuan Zhang, name: Benjamin Schneider, name: Yi Lu, name: Xiang Yue, name: Wenhu Chen

Academic writing requires both coherent text generation and precise citation
of relevant literature. Although recent Retrieval-Augmented Generation (RAG)
systems have significantly improved factual accuracy in general-purpose text
generation, their capacity to adequately support professional academic writing
remains limited. In this work, we introduce ScholarCopilot, a unified framework
designed to enhance existing large language models for generating professional
academic articles with accurate and contextually relevant citations.
ScholarCopilot dynamically determines when to retrieve scholarly references by
generating a retrieval token [RET], and then utilizes its representation to
look up relevant citations from a database. The retrieved references are fed
into the model to augment the generation process. We jointly optimize both the
generation and citation tasks within a single framework to increase efficiency.
Trained on 500K papers from arXiv, our model achieves a top-1 retrieval
accuracy of 40.1% on our evaluation dataset, outperforming baselines such as
E5-Mistral-7B-Instruct (15.0%) and BM25 (9.8%). On a dataset of 1,000 academic
writing samples, ScholarCopilot scores 16.2/25 in generation quality (measured
across relevance, coherence, academic rigor, completeness, and innovation),
surpassing models with 10x more parameters such as Qwen-2.5-72B-Instruct
(15.8/25). Human studies also confirm ScholarCopilot's superior performance in
citation recall, writing efficiency, and overall user experience, confirming
the effectiveness of our approach.