解码学术手稿中的端到端写作轨迹
Decoding the End-to-end Writing Trajectory in Scholarly Manuscripts
AI总结：
针对LLM难以提供文档级学术写作反馈的问题，提出基于意图、动作和数据类型的写作行为分类体系，并构建ManuScript数据集以捕捉端到端写作轨迹，辅助提升写作助手的反馈能力。
摘要：
学术写作呈现一个复杂的空间，通常遵循一套有条理的程序来规划并产出逻辑合理且富有创造性的作品。近期涉及大型语言模型（LLM）的研究在文本生成和修改任务中取得了显著成功；然而，LLM在提供对学术写作至关重要的文档级结构和创造性反馈方面仍存在困难。在本文中，我们引入了一种新颖的分类体系，根据意图、写作者动作以及所写数据的信息类型对学术写作行为进行分类。我们还提供了ManuScript，这是一个使用我们分类体系的简化版本进行标注的原创数据集，用以展示写作者动作及其背后的意图。受认知写作理论的启发，我们针对科学论文的分类体系包含三个层级的分类，以追踪一般写作流程并识别嵌入在每个更高层级流程中的独特写作者活动。ManuScript旨在通过捕捉写作轨迹的线性和非线性来提供学术写作过程的完整图景，从而使写作助手能够在端到端层面上提供更强的反馈和建议。收集到的写作轨迹可在 https://minnesotanlp.github.io/REWARD_demo/ 查看。
英文摘要：
Scholarly writing presents a complex space that generally follows a methodical procedure to plan and produce both rationally sound and creative compositions. Recent works involving large language models (LLM) demonstrate considerable success in text generation and revision tasks; however, LLMs still struggle to provide structural and creative feedback on the document level that is crucial to academic writing. In this paper, we introduce a novel taxonomy that categorizes scholarly writing behaviors according to intention, writer actions, and the information types of the written data. We also provide ManuScript, an original dataset annotated with a simplified version of our taxonomy to show writer actions and the intentions behind them. Motivated by cognitive writing theory, our taxonomy for scientific papers includes three levels of categorization in order to trace the general writing flow and identify the distinct writer activities embedded within each higher-level process. ManuScript intends to provide a complete picture of the scholarly writing process by capturing the linearity and non-linearity of writing trajectory, such that writing assistants can provide stronger feedback and suggestions on an end-to-end level. The collected writing trajectories are viewed at https://minnesotanlp.github.io/REWARD_demo/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
