ScholarCopilot: Training Large Language Models for Academic Writing with
  Accurate Citations
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.
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Yi Lu
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ScholarCopilot: Training Large Language Models for Academic Writing with
  Accurate Citations
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.
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.
Retrieval-Augmented Generation (RAG) ScholarCopilot retrieval token academic writing large language models citation recall writing efficiency user experience