ScholarCopilot-Data-v1

# ScholarCopilot-Data-v1

| [**🚀Project Page**](https://tiger-ai-lab.github.io/ScholarCopilot/) | [**📖Paper**](https://arxiv.org/abs/2504.00824) | [**🔗Github**](https://github.com/TIGER-AI-Lab/ScholarCopilot/) | [**🤗Model**](https://huggingface.co/TIGER-Lab/ScholarCopilot-v1) | [**🤗Demo**](https://huggingface.co/spaces/TIGER-Lab/ScholarCopilot) |

ScholarCopilot-Data-v1 contains the corpus data and embedded vectors 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).

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.

## 🌟 Key Features

- ** 📝 Next-3-Sentence Suggestions: Facilitates writing by predicting the next sentences with automatic retrieval and citation of relevant reference papers.
- ** 📚 Citation Suggestions on Demand: Provides precise, contextually appropriate paper citations whenever needed.
- ** ✨ Full Section Auto-Completion: Assists in brainstorming and drafting comprehensive paper content and structure.

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.

# ScholarCopilot-Data-v1

| [**🚀项目主页**](https://tiger-ai-lab.github.io/ScholarCopilot/) | [**📖论文**](https://arxiv.org/abs/2504.00824) | [**🔗Github**](https://github.com/TIGER-AI-Lab/ScholarCopilot/) | [**🤗模型**](https://huggingface.co/TIGER-Lab/ScholarCopilot-v1) | [**🤗演示**](https://huggingface.co/spaces/TIGER-Lab/ScholarCopilot) |

ScholarCopilot-Data-v1 包含了[Scholar Copilot](https://github.com/TIGER-AI-Lab/ScholarCopilot)的语料库数据与嵌入向量。Scholar Copilot 通过将自动文本补全与智能引文推荐无缝整合为连贯统一、人机协同的人工智能驱动流水线，优化学术写作流程。该工具旨在提升研究人员的写作效率与创造力，依托迭代式上下文感知检索增强生成（Retrieval-Augmented Generation, RAG）技术，为研究人员提供高质量文本生成与精准的引文推荐服务。

当前版本的Scholar Copilot 基于在完整Arxiv全论文语料上训练的当前顶尖70亿参数大语言模型（Large Language Model, LLM）。这款统一的检索生成模型能够基于参考文献，针对何时引用、引用何种文献以及如何生成连贯内容做出上下文敏感的决策。

## 🌟 核心特性

- ** 📝 三句续写建议**：通过自动检索并引用相关参考文献来预测后续语句，辅助写作推进。
- ** 📚 按需引文推荐**：在任意需要时提供精准且符合上下文语境的论文引文建议。
- ** ✨ 全章节自动补全**：助力研究人员进行头脑风暴，协助起草完整的论文内容与结构。

当前版本的Scholar Copilot 主要聚焦于学术论文的引言与相关工作章节，未来版本将支持全论文写作。