发表机构
Nanjing University of Science and Technology（南京理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究分析2020-2025年13921篇顶级NLP会议论文，发现GPU资源集中度远超影响力集中度，GPU能力与学术影响力存在关联但无法单独解释研究影响力。
AI 中文摘要
计算资源在NLP研究中愈发重要，但所报告的GPU能力与学术影响力的关联程度尚不明确。我们分析了2020至2025年间发表的13921篇ACL、EMNLP和NAACL主会议论文，以GPU资源作为计算资源的操作化衡量指标。从全文中提取GPU型号与数量，将每篇论文报告的最大配置标准化为可比较的硬件能力指标，并将这些数据与引用、奖项、主题及机构元数据关联起来。GPU报告愈发普遍但仍不完整，所报告的能力提升主要来自更新的硬件代际与中等规模的多GPU配置。资源集中度远超影响力集中度：年度GPU可量化论文的前20%贡献了83.9%-89.9%的报告GPU能力，但仅占27%-32%的引用量和20%-33%的论文奖项。在调整后的模型中，报告的总GPU能力每增加10倍，NLP主题年度内的引用百分位将提升3.52个百分点，但模型的R²仅增加0.0042。与新硬件代际相比，GPU数量与引用及奖项结果的正关联更为稳定。总体而言，所报告的GPU资源与学术影响力存在关联，但几乎无法单独解释研究影响力。
英文摘要
Computational resources are increasingly central to NLP research, but how closely reported GPU capability aligns with scholarly impact remains unclear. We analyze 13,921 ACL, EMNLP, and NAACL main-conference papers published between 2020 and 2025, using GPU resources as our operational measure of computational resources. From full texts, we extract GPU models and counts, standardize each paper's largest reported configuration into a comparable hardware-capability measure, and link these data to citation, award, topic, and institutional metadata. GPU reporting became more common but remained incomplete, while reported capability increased mainly through newer hardware generations and medium-scale multi-GPU configurations. Resource concentration substantially exceeded impact concentration: the annual top 20% of GPU-quantifiable papers accounted for 83.9%-89.9% of reported GPU capability, but only 27%-32% of citations and 20%-33% of paper awards. In adjusted models, a tenfold increase in aggregate reported GPU capability was associated with a 3.52-percentage-point increase in within-NLP topic-year citation percentile, but increased model R^2 by only 0.0042. GPU count showed more consistent positive associations with citation and award outcomes than newer hardware generation. Overall, reported GPU resources are associated with scholarly impact but provide little standalone explanation of research influence.
CommentsEMNLP 2026, Main
