AI生成文本中存在虚构人物配对，且这些配对具有模型特异性。
The Ghost Couple: Correlated LLM Name Priors and Their Haunting of the Web and Academic Publishing
- 发现大模型生成虚构专家时会成对出现，配对频率远超随机
- 1655条伪造论文记录被注册在Zenodo，991条集中于一个月
- 配对模式可作模型发布窗口的时序标记，适合研究AI演化
Elena Vasquez和Marcus Chen等名字并不存在，却作为火山专家、宇航员、小说主角及学术合作者出现在数百份独立生成的AI文档中。我们发现，大语言模型不仅倾向于生成高概率个体名，还会产生相关联的角色组合，其共现频率远超随机预期，且在不同生成中保持一致。这些命名先验具有模型家族特异性（如Claude：Elena Vasquez + Marcus Chen + Amara Okafor；Gemini：Aris Thorne + Lena Petrova；GPT：Elara Voss无固定搭档），版本特定，并在模型发布时被主动抑制，留下可追溯的时间指纹。我们在Zenodo（CERN运营的DataCite DOI仓库）中发现1,655条伪造记录，声称发表于不存在的期刊，带有真实数据集标识符和服务器端时间戳，显示人为回溯日期；其中991条集中在单月。这些记录已被学术聚合器收录。此外，这些幽灵名字还出现在ResearchGate上组成合成研究组，跨模型协作关系可作为模型部署时间的可靠时序代理。
原文摘要 · Abstract (English)
These names do not exist. Elena Vasquez and Marcus Chen have appeared as volcano experts, astronauts, thriller protagonists, podcast hosts, and academic co-authors across hundreds of independently produced AI-generated documents, never having lived. We show that large language models do not merely default to high-probability individual names when generating fictional experts: they produce correlated character ensembles, pairs and trios whose co-occurrence rates far exceed chance and are consistent across independent generations. These priors are model-family-specific (Claude: Elena Vasquez + Marcus Chen + Amara Okafor; Gemini: Aris Thorne + Lena Petrova; GPT: Elara Voss with no fixed partner), version-specific, and actively suppressed at model release boundaries, leaving dateable behavioral fingerprints in the content they produced. We document a downstream consequence at scale. On Zenodo, a CERN-operated repository that mints real DataCite DOIs, we identify 1,655 ghost-authored records claiming nonexistent journals with fabricated publication dates: server-side DataCite timestamps prove deliberate backdating, and 991 records were registered in a single month; these carry real DOIs registered in DataCite, making them harvestable by any scholarly aggregator that ingests DOI metadata. Ghost names additionally appear on ResearchGate forming synthetic research groups with collaborators drawn from multiple model families; publication dates on these records provide a reliable temporal proxy for model deployment windows.
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