NLP研究正从传统会议转向通用机器学习会议，作者更看重引用优势。
The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing
- 通过对比新旧作者发表趋势，发现研究者逐渐流向通用ML会议。
- 2019至2024年，新作者在通用ML会议的论文占比从5%升至21%。
- 通用ML会议带来显著引用优势，影响作者投稿决策。
自然语言处理（NLP）传统上集中于ACL等核心期刊发表。然而，大语言模型（LLMs）的发展模糊了NLP与通用机器学习（ML）的界限，作者越来越多地在两领域共同的会议上发表成果。我们分析2010至2026年间的NLP研究，涵盖新老作者，发现学科重心正在转移。首先，在大模型时代前后对比发现，资深作者在顶级ACL主会场的占比下降19.2个百分点，转而增加至新的Findings会议（+14.8个百分点），而通用ML会议占比上升8.6个百分点，即使考虑领域整体增长。其次，对首次发表至少三篇一作NLP论文的新作者而言，其论文主要发表于ACL的比例从2019年的84%降至2024年的74%，而发表于通用ML会议的比例则从5%升至21%。通过因果推断分析，我们估计通用ML会议能带来显著的引用优势，这直接影响了作者的投稿选择。这些结果表明，NLP研究的发表重心正在发生显著变化。
原文摘要 · Abstract (English)
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.
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