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  - en
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  base_model:
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  - Qwen/Qwen2.5-7B
 
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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://github.com/TIGER-AI-Lab/ScholarCopilot). 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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  ## 🌟 Key Features
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  - ** πŸ“ Next-3-Sentence Suggestions: Facilitates writing by predicting the next sentences with automatic retrieval and citation of relevant reference papers.
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  - ** πŸ“š 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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  - 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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+
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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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+
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  ## 🌟 Key Features
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  - ** πŸ“ Next-3-Sentence Suggestions: Facilitates writing by predicting the next sentences with automatic retrieval and citation of relevant reference papers.
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  - ** πŸ“š 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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+
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+ ## Citation
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+
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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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+ ```