AI难准确归因，缺失元数据会引发错误或虚构作者信息
Towards Nexus-Score: Metadata Gaps Limit Scholarly AI Attribution
- 通过隐藏关键链接测试AI归因能力，发现缺失元数据导致错误识别
- 恢复正确链接后归因成功，错误链接无法弥补，469次测试全失败
- 适用于需要精准学术归因的系统，如科研评价、知识图谱构建
人工智能系统越来越多地影响科学发现与成果归属。我们探究了缺失元数据是否导致AI无法正确归因。作为边界测试，当AI无法访问任务相关的论文列表时，常生成列表外的标识符，部分为虚构内容。随后在真实学术基础设施中，利用OpenAlex记录隐藏或恢复作者、机构、资助方、参考文献和文本访问链接，固定作品与任务。恢复相关链接后归因得以实现；恢复错误类型则无效，469次不匹配测试中均无正确答案。因此，在这些任务中，某一类元数据无法替代另一类。缺失链接导致虚构答案、拒绝响应或工具预算耗尽，网络搜索也无法恢复被隐藏的作者链接。综上，只有在记录连接可见或可恢复时，AI才能完成归因。这推动了Nexus-Score的提出——一种用于检测元数据缺失的记录级检查机制，以指导修复并为人工智能驱动的学术记录做好准备。
原文摘要 · Abstract (English)
Artificial intelligence systems increasingly mediate how science is found and credited. We asked whether missing metadata prevents AI systems from crediting work. As a boundary test, an AI system citing without access to task-relevant paper lists often produced out-of-list identifiers, some fabricated. We then tested the mechanism in real scholarly infrastructure by using OpenAlex records to hide or restore author, institution, funder, reference, and text-access links while holding works and tasks fixed. Restoring the relevant link made the corresponding attribution possible; restoring the wrong kind did not, with 0 correct answers across 469 completed mismatched tests. Thus, in these tasks, one metadata facet did not substitute for another. Missing links led to invented answers, refusals, or tool-budget exhaustion, and web search did not recover hidden author links. In sum, AI systems credited work only when record connections were visible or recoverable. This motivates Nexus-Score, a record-level check for metadata gaps, to guide repair and help prepare the scholarly record for AI-mediated use.
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