NLP的未来可能不在NLP会议上：自然语言处理领域的学术迁移模式
The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing
AI总结：
研究通过分析2010-2026年NLP论文发表数据，发现NLP研究正从传统ACL会议向通用ML会议迁移，新作者在ML会议发表比例从5%升至21%，且ML会议带来显著引用优势。
摘要：
自然语言处理（NLP）传统上在其核心学科会议如ACL上发表。然而，大型语言模型（LLMs）的进展导致NLP与通用机器学习（ML）之间的学科界限模糊，作者经常在两个领域的会议上发表论文。在此，我们询问学科重心是否正在转移。利用2010年至2026年发表的NLP研究以及对既有和新作者的研究，我们发现迁移正在发生。首先，比较LLM前和LLM后时代，既有作者在旗舰*ACL主会议轨道的份额下降了19.2个百分点，而在较新的Findings轨道中增加了14.8个百分点，通用ML会议上升了8.6个百分点，即使调整了领域的并行增长。其次，在首次以至少三篇第一作者NLP主题论文出道的新作者中，其工作主要出现在*ACL会议的比例从84%（2019年）下降到74%（2024年），而主要出现在通用ML会议的比例从5%上升到21%。利用因果推断技术，我们估计这些通用ML会议赋予显著的引用溢价，这影响了会议选择。综合来看，这些结果指出了NLP研究发表场所的重大转变。
英文摘要：
Natural Language Processing (NLP) has traditionally been published in its core disciplinary venues like ACL. However, advances in Large Language Models (LLMs) has led to a blurring of the disciplinary lines between NLP and general Machine Learning (ML), with authors regularly publishing in venues from both fields. Here, we ask whether the disciplinary center of gravity is shifting. Using NLP research published from 2010 to 2026 and studies of both established and new authors, we find that a migration is taking place. First, comparing the pre- and post-LLM eras, established authors lost 19.2pp of share at flagship *ACL main-conference tracks while gaining 14.8pp in the newer Findings tracks, and general ML venues rose 8.6pp, even when adjusting for parallel growth in the fields. Second, among newer authors who debut with at least three first-author NLP-topic papers, the share whose work appears mostly at *ACL venues fell from 84% (2019) to 74% (2024), while the share appearing mostly at general ML venues rose from 5% to 21%. Using causal inference techniques, we estimate that these general ML venues confer a significant citation premium, which influences venue selection. Together, these results point to a significant shift in where NLP research is published.
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。
