ACL还保持其王冠吗？一项十年长的规模和影响的定量分析，横跨顶级AI会议
Has ACL Lost Its Crown? A Decade-Long Quantitative Analysis of Scale and Impact Across Leading AI Conferences
AI总结：
本文通过十年实证分析，发现ACL在规模和影响方面仍优于其他顶级AI会议，揭示了会议扩张与学术影响增长之间的非线性关系。
摘要：
最近语言模型（LMs）的兴起迅速扩展了NLP/AI研究，推动了主要会议的呈交和接受数量呈指数级增长。然而，这种增长却被关于会议质量的日益加剧的担忧所笼罩，例如抄袭、审稿人经验不足和串标行为。然而，现有研究大多依赖于定性描述，例如专家访谈和社会媒体讨论，缺乏纵向的实证证据。为了填补这一空白，我们进行了一项为期十年的实证研究（2014-2024），涵盖七大顶级会议。我们构建了一个四维文献计量框架，涵盖会议规模、核心引用统计、影响分散以及跨会议和期刊影响。值得注意的是，我们进一步提出了一种称为质量-数量弹性（QQE）的指标，该指标衡量引用增长相对于接受增长的弹性。我们突显了两个关键发现。第一，会议扩大并不导致学术影响的成比例增长，因为QQE在所有会议中随时间推移持续下降。第二，ACL并未失去其王冠，继续在中位引用、里程碑贡献和引用覆盖方面优于其他NLP会议。本研究提供了首个十年长、跨会议的实证证据，展示了主要NLP/AI会议的演变。我们的代码可在https://anonymous.4open.science/r/acl-crown-analysis-38D5上获得。
英文摘要：
The recent surge of language models (LMs) has rapidly expanded NLP/AI research, driving an exponential rise in submissions and acceptances at major conferences. Yet this growth has been shadowed by escalating concerns over conference quality, such as plagiarism, reviewer inexperience, and collusive bidding. However, existing studies rely largely on qualitative accounts, for example expert interviews and social media discussions, lacking longitudinal empirical evidence. To fill this gap, we conduct a ten-year empirical study (2014-2024) spanning seven leading conferences. We build a four-dimensional bibliometric framework covering conference scale, core citation statistics, impact dispersion, and cross-venue and journal influence. Notably, we further propose a metric called Quality-Quantity Elasticity (QQE), which measures the elasticity of citation growth relative to acceptance growth. We highlight two key findings. First, conference expansion does not lead to proportional growth in scholarly impact, as QQE consistently declines over time across all venues. Second, ACL has not lost its crown, continuing to outperform other NLP conferences in median citations, milestone contributions, and citation coverage. This study provides the first decade-long, cross-venue empirical evidence on the evolution of major NLP/AI conferences. Our code is available at https://anonymous.4open.science/r/acl-crown-analysis-38D5.
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。
