title: ScholarCopilot: Training Large Language Models for Academic Writing with
    Accurate Citations
summary: 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.
paper_authors._id: 67ede79d21d7e74ee3e2832a
paper_authors.name: Yubo Wang
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paper_authors.name: Xueguang Ma
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paper_authors.fullname: Xueguang Ma
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paper_authors.name: Ping Nie
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paper_authors.fullname: Ping Nie
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paper_authors.user: pingnieuk
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paper_authors.name: Huaye Zeng
paper_authors._id: 67ede79d21d7e74ee3e2832e
paper_authors.name: Zhiheng Lyu
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paper_authors.fullname: Zhiheng Lyu
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paper_authors.user: cogito233
paper_authors._id: 67ede79d21d7e74ee3e2832f
paper_authors.name: Yuxuan Zhang
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paper_authors.fullname: Yuxuan Zhang
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paper_authors.user: Reacherx
paper_authors._id: 67ede79d21d7e74ee3e28330
paper_authors.name: Benjamin Schneider
paper_authors.status: admin_assigned
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paper_authors.fullname: Benjamin Schneider
paper_authors.type: user
paper_authors.user: BenSchneider
paper_authors._id: 67ede79d21d7e74ee3e28331
paper_authors.name: Yi Lu
paper_authors._id: 67ede79d21d7e74ee3e28332
paper_authors.name: Xiang Yue
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paper_authors.fullname: Xiang Yue
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paper_authors.user: yuexiang96
paper_authors._id: 67ede79d21d7e74ee3e28333
paper_authors.name: Wenhu Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: Wenhu Chen
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paper_authors.user: wenhu
paper_title: ScholarCopilot: Training Large Language Models for Academic Writing with
    Accurate Citations
paper_summary: 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.
paper_ai_summary: ScholarCopilot is a unified framework that enhances large language models by dynamically retrieving and integrating scholarly citations, improving accuracy and effectiveness in academic text generation.
paper_ai_keywords: Retrieval-Augmented Generation (RAG) ScholarCopilot retrieval token academic writing large language models citation recall writing efficiency user experience