project
ScholarCopilot - An AI-powered academic writing assistant jointly launched by Waterloo and Carnegie Mellon University
ScholarCopilot is an AI tool designed specifically for academic writing, developed by a research team at the University of Waterloo and Carnegie Mellon University in Canada. Based on the Qwen-2.5-7B model, it dynamically retrieves citations and performs joint optimization...
What is ScholarCopilot?
ScholarCopilot is an AI tool designed specifically for academic writing, developed by a research team at the University of Waterloo and Carnegie Mellon University in Canada. Based on the Qwen-2.5-7B model, it accurately generates academic text with precise citations through dynamic citation retrieval and joint optimization of generation and citation. During text generation, ScholarCopilot inserts special retrieval tags to query the citation database, integrating the retrieved citations into subsequent generation to improve citation accuracy and text coherence.
The main functions of ScholarCopilot
- Contextual awareness continuationPredict the next three sentences based on existing content to ensure logical coherence, such as automatically expanding the literature review section.
- Chapters are generated automatically.Enter keywords and AI will generate a complete chapter framework, supporting adjustments to the academic style, such as empirical analysis or theoretical derivation.
- Multilingual supportSupports mixed Chinese and English writing, suitable for submissions to international journals.
- Dynamic retrieval enhancementBy inserting tags during writing, AI can retrieve relevant literature from 500,000 arXiv papers in real time with an accuracy rate of over 40%.
- One-click insert referenceSupports multiple formats such as APA/MLA, automatically generates BibTeX entries, saving organization time.
- Source tracing and verification functionClicking the citation will take you directly to the original text, ensuring that every reference is authentic and verifiable.
- PhD team training dataBased on the Qwen-2.5-7B model, with minor adjustments to the professional academic corpus, the generated text achieved an academic rigor score of 2.87/5, far exceeding similar tools.
- Error self-checking systemAutomatically flags suspected "hallucinatory content" and prompts users to manually verify it, such as contradictory data or unverified conclusions.
The technical principles of ScholarCopilot
- Dynamic search tagsDuring text generation, ScholarCopilot dynamically determines when citations are needed and generates a special search tag. This tag triggers the model to pause text generation and search for relevant literature in academic databases in real time.
- Joint optimization generation and retrievalThe retrieved literature content (such as abstracts or key paragraphs) is directly incorporated into subsequent text generation steps. In this way, the model can generate high-quality academic text, ensuring the accuracy and relevance of citations.
- Comparative learning optimizationThe representation of retrieval tags is optimized through contrastive learning, enabling the model to perform similarity searches efficiently and further improve retrieval accuracy.
- Improved citation accuracyScholarCopilot achieved a top-1 retrieval accuracy of 40.1%, which is significantly better than traditional methods such as E5-Mistral-7B-Instruct (15.0%) and BM25 (9.8%).
- Generate quality optimizationOn a dataset of 1,000 academic writing samples, ScholarCopilot achieved a comprehensive score of 16.2/25 across five dimensions: relevance, coherence, academic rigor, completeness, and originality, surpassing models with larger parameters.
- Training and DataScholarCopilot is based on the Qwen-2.5-7B model, and the training dataset contains 500K papers from arXiv. By jointly optimizing the text generation and citation retrieval tasks, the model achieves significant improvements in both efficiency and accuracy.
ScholarCopilot's project address
- Project official website:https://tiger-ai-lab.github.io/ScholarCopilot/
- Github repository:https://github.com/TIGER-AI-Lab/ScholarCopilot
- HuggingFace model library:https://huggingface.co/TIGER-Lab/ScholarCopilot-v1
- arXiv technical paper:https://arxiv.org/pdf/2504.00824
Application scenarios of ScholarCopilot
- Academic paper writingScholarCopilot is designed specifically for academic writing, significantly improving the efficiency and quality of paper writing. Through a dynamic "generate-as-you-go" mechanism, it determines in real-time when to cite sources and automatically retrieves relevant literature while generating text.
- Introduction and related work sectionsScholarCopilot excels particularly in writing the introduction and related work sections of papers. It can automatically predict the next few sentences and provide accurate citation suggestions based on the context.
- Academic writing instruction and trainingScholarCopilot can be used for teaching and training academic writing. It helps students and novice researchers master the skills and standards of academic writing, enabling them to quickly start writing high-quality academic papers.
- Research team collaborationFor research teams, ScholarCopilot allows for the sharing of subject-specific knowledge bases, helping team members quickly build paper frameworks. This is especially beneficial for new members, enabling them to quickly get started with review writing in their field and improving the overall writing efficiency of the team.
- Journal peer reviewScholarCopilot's source verification function allows journal reviewers to verify the authenticity of references with a single click.