用大模型提升学术文献处理效率，助研究者高效获取知识
Preface to the Special Issue of the TAL Journal on Scholarly Document Processing
- 利用大语言模型解析学术论文的复杂语言与专业术语
- 支持自动文献综述、写作辅助和研究探索等核心任务
- 适合从事NLP、信息检索与科研辅助系统的研究者
学术文献的快速增长使研究人员难以跟上新知识。自动化工具因此变得前所未有的重要，以帮助导航和理解海量信息。科学论文因其复杂的语言、专业术语和多样格式，带来独特挑战，需先进方法提取可靠且可操作的见解。大语言模型（LLMs）提供了新机遇，能够实现文献综述、写作辅助及研究的交互式探索。本期《TAL期刊》特刊聚焦这些挑战的相关研究，更广泛地涵盖自然语言处理与信息检索在学术与科学文档中的应用。
原文摘要 · Abstract (English)
The rapid growth of scholarly literature makes it increasingly difficult for researchers to keep up with new knowledge. Automated tools are now more essential than ever to help navigate and interpret this vast body of information. Scientific papers pose unique difficulties, with their complex language, specialized terminology, and diverse formats, requiring advanced methods to extract reliable and actionable insights. Large language models (LLMs) offer new opportunities, enabling tasks such as literature reviews, writing assistance, and interactive exploration of research. This special issue of the TAL journal highlights research addressing these challenges and, more broadly, research on natural language processing and information retrieval for scholarly and scientific documents.
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