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--- |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-7B |
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license: apache-2.0 |
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--- |
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# ScholarCopilot-v1 Model |
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ScholarCopilot-v1 is the foundation model of [Scholar Copilot](https://arxiv.org/abs/2504.00824). Scholar Copilot improves the academic writing process by seamlessly integrating automatic text completion and intelligent citation suggestions into a cohesive, human-in-the-loop AI-driven pipeline. Designed to enhance productivity and creativity, it provides researchers with high-quality text generation and precise citation recommendations powered by iterative and context-aware Retrieval-Augmented Generation (RAG). |
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The current version of Scholar Copilot leverages a state-of-the-art 7-billion-parameter language model (LLM) trained on the complete Arxiv full paper corpus. This unified model for retrieval and generation is adept at making context-sensitive decisions about when to cite, what to cite, and how to generate coherent content based on reference papers. |
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Links: [paper](https://arxiv.org/abs/2504.00824) | [model](https://huggingface.co/TIGER-Lab/ScholarCopilot-v1) | [demo](https://huggingface.co/spaces/TIGER-Lab/ScholarCopilot) |
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## 🌟 Key Features |
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- ** 📝 Next-3-Sentence Suggestions: Facilitates writing by predicting the next sentences with automatic retrieval and citation of relevant reference papers. |
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- ** 📚 Citation Suggestions on Demand: Provides precise, contextually appropriate paper citations whenever needed. |
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- ** ✨ Full Section Auto-Completion: Assists in brainstorming and drafting comprehensive paper content and structure. |
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The current version of ScholarCopilot primarily focuses on the introduction and related work sections of academic papers. We will support full-paper writing in future releases. |
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## Citation |
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Please cite our paper with |
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``` |
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@article{wang2024scholarcopilot, |
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title={ScholarCopilot: Training Large Language Models for Academic Writing with Accurate Citations}, |
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author = {Wang, Yubo and Ma, Xueguang and Nie, Ping and Zeng, Huaye and Lyu, Zhiheng and Zhang, Yuxuan and Schneider, Benjamin and Lu, Yi and Yue, Xiang and Chen, Wenhu}, |
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journal={arXiv preprint arXiv:2504.00824}, |
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year={2025} |
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} |
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``` |
