计算资源投入越高，基础模型研究影响力越大，但与研究环境无关。
The Role of Computing Resources in Publishing Foundation Model Research
- 分析6517篇2022–2024年基础模型论文，量化计算资源与成果关联。
- 计算资源多的论文获更高引用，且与国家经费分配相关。
- 建议共享算力降低门槛，促进多元参与和持续创新。
人工智能前沿研究需大量计算资源（如GPU）、数据与人力。本文评估了这些资源与基础模型（FM）科研进展的关系。我们分析了2022至2024年间发表的6517篇基础模型论文，并对229位第一作者进行了调查。结果表明，计算资源投入与国家资助水平及论文引用数呈正相关，但与研究机构类型（学术或工业）、研究领域或方法学无显著关联。研究建议个人与机构应推动共享、可负担的计算资源，以降低弱势研究者准入门槛，扩大参与者多样性，促进创新持续发展。数据将公开于：https://mit-calc.csail.mit.edu/
原文摘要 · Abstract (English)
Cutting-edge research in Artificial Intelligence (AI) requires considerable resources, including Graphics Processing Units (GPUs), data, and human resources. In this paper, we evaluate of the relationship between these resources and the scientific advancement of foundation models (FM). We reviewed 6517 FM papers published between 2022 to 2024, and surveyed 229 first-authors to the impact of computing resources on scientific output. We find that increased computing is correlated with national funding allocations and citations, but our findings don't observe the strong correlations with research environment (academic or industrial), domain, or study methodology. We advise that individuals and institutions focus on creating shared and affordable computing opportunities to lower the entry barrier for under-resourced researchers. These steps can help expand participation in FM research, foster diversity of ideas and contributors, and sustain innovation and progress in AI. The data will be available at: https://mit-calc.csail.mit.edu/
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