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arXiv 2504.02486cs.AI
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我们需要在面向科学发现的AI中改进数据治理与归因

We Need Improved Data Curation and Attribution in AI for Scientific Discovery

IBM Research Europe(IBM欧洲研究院);NCCR Catalysis(瑞士国家催化研究能力中心)

Mara Graziani, Antonio Foncubierta, Dimitrios Christofidellis, Irina Espejo-Morales, Malina Molnar, Marvin Alberts, Matteo Manica, Jannis Born

AI总结:

本研究探讨了合成数据与真实实验数据在科学发现中的作用,发现开放平台多数实验数据集采用率低且合成数据日益难辨,提出通过为真实实验数据添加水印以增强可追溯性与模型鲁棒性,促进合成与人类生成内容的平衡整合。

摘要:

随着人类生成数据与合成数据之间的相互作用不断演变,科学发现中出现了关于数据完整性和模型稳定性的新挑战。在本研究中,我们考察了合成数据相对于真实实验数据在科学研究中的作用。我们的分析表明,开放获取平台上近四分之三的实验数据集采用率相对较低,这为通过自动化方法提升其可发现性和可用性提供了新机遇。此外,我们观察到区分合成数据与真实实验数据的难度正日益增加。我们建议,除了持续开展自动化合成数据检测工作外,还应加大对真实实验数据添加水印的力度,从而增强数据的可追溯性与完整性。我们的估算表明,每年对不到一半的真实世界数据添加水印,即可帮助维持模型鲁棒性,同时促进合成内容与人类生成内容的平衡整合。

英文摘要:

As the interplay between human-generated and synthetic data evolves, new challenges arise in scientific discovery concerning the integrity of the data and the stability of the models. In this work, we examine the role of synthetic data as opposed to that of real experimental data for scientific research. Our analyses indicate that nearly three-quarters of experimental datasets available on open-access platforms have relatively low adoption rates, opening new opportunities to enhance their discoverability and usability by automated methods. Additionally, we observe an increasing difficulty in distinguishing synthetic from real experimental data. We propose supplementing ongoing efforts in automating synthetic data detection by increasing the focus on watermarking real experimental data, thereby strengthening data traceability and integrity. Our estimates suggest that watermarking even less than half of the real world data generated annually could help sustain model robustness, while promoting a balanced integration of synthetic and human-generated content.

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。

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