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ScholarCopilot: Training Large Language Models for Academic Writing with Accurate Citations by Wang, Yubo; Ma, Xueguang; Nie, Ping; Zeng, Huaye; Lyu, Zhiheng; Zhang, Yuxuan; Schneider, Benjamin; Lu, Yi; Yue, Xiang; Chen, Wenhu is a scholarly article available to read on EtoBox.

What is ScholarCopilot: Training Large Language Models for Academic Writing with Accurate Citations about?

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 ability to 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], which is then used to query a citation 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 improve efficiency. Our model is built upon Qwen-2.5-7B and trained on 500K papers from arXiv. It 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 generati

Author

Wang, Yubo; Ma, Xueguang; Nie, Ping; Zeng, Huaye; Lyu, Zhiheng; Zhang, Yuxuan; Schneider, Benjamin; Lu, Yi; Yue, Xiang; Chen, Wenhu

Published

2025

Language

EN