面对千人论文，新指标SBCI更公平地评估个人学术贡献。
When a Paper Has 1000 Authors: Rethinking Citation Metrics in the Era of LLMs
- 提出SBCI指数，平衡大规模与小规模论文的贡献评估。
- 在合成数据上验证，该指标能有效区分个体影响力。
- 适合用于学术招聘和资助决策，解决作者过多带来的评价难题。
作者级引用指标是复杂科研生态中衡量学术影响力的实用、可解释且可扩展的信号，常被用作招聘决策的代理依据。然而过去五年间，大语言模型与基础模型领域出现了大规模出版物，论文作者可达数百至数千人，数月内获得数万次引用。例如，Gemini论文有1361名作者，19个月内被引约4600次。在此情境下，传统指标如总引用次数和h指数无法有效区分个体贡献。因此，我们提出核心问题：如何在大型LLM论文中识别出突出的研究者？该问题在学术招聘和资助决策中尤为重要。本文引入一种新型引用指标SBCI，旨在解决这一挑战，通过平衡大规模与小规模出版物的贡献权重。我们分析了其理论性质，并在合成出版数据集上评估其表现。结果表明，该指标在大规模协作时代提供了更稳健、更具区分度的个体学术影响力评估。
原文摘要 · Abstract (English)
Author-level citation metrics provide a practical, interpretable, and scalable signal of scholarly influence in a complex research ecosystem. It has been widely used as a proxy in hiring decisions. However, the past five years have seen the rapid emergence of large-scale publications in the field of large language models and foundation models, with papers featuring hundreds to thousands of co-authors and receiving tens of thousands of citations within months. For example, Gemini has 1361 authors and has been cited around 4600 times in 19 months. In such cases, traditional metrics, such as total citation count and the $h$-index, fail to meaningfully distinguish individual contributions. Therefore, we propose the following research question: How can one identify standout researchers among thousands of co-authors in large-scale LLM papers? This question is particularly important in scenarios such as academic hiring and funding decisions. In this paper, we introduce a novel citation metric designed to address this challenge by balancing contributions across large-scale and small-scale publications. We propose the SBCI index, analyze its theoretical properties, and evaluate its behavior on synthetic publication datasets. Our results demonstrate that the proposed metric provides a more robust and discriminative assessment of individual scholarly impact in the era of large-scale collaborations.
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