Newest papers

Every Model Leaves a Trace: Multi-Resolution LLM Fingerprinting

Mohammad Reza Modarres, Armin Tourajmehr, Artin Tavasoli, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28434 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

大型语言模型在其生成的文本中留下模型特定的痕迹,但仍然不清楚这些指纹如何精确地区分相关模型,以及显而易见的线索减少时哪些信号仍然具有信息性。我们通过指纹分辨率来研究这一点,考虑到模型分辨率,从不同的家庭到蒸和适应的变体,以及信号分辨率在行为,蒸,语法和语法证据上。我们逐渐抑制表面线索,语法输出,可用的文本数量变化,并在快速分布和语言中测试一般化。我们的结果表明,指纹在广泛的家庭差异之外仍然存在,在蒸模型中仍然可检测,甚至在模型基础上的LoRA调整。尽管分辨率与表面和语法输出相似,语法输

Machine translation (Meta M2M100); the English original follows.

Abstract

Large language models leave model-specific traces in their generated text, but it remains unclear how finely these fingerprints can distinguish related models and which signals remain informative as obvious cues are reduced. We study this through fingerprint resolution, considering both model resolution, from different families to distilled and adapted variants, and signal resolution across behavioral, stylometric, lexical, and semantic evidence. We progressively suppress surface cues, paraphrase outputs, vary the amount of available text, and test generalization across prompt distributions and languages. Our results show that fingerprints persist beyond broad family differences, remaining detectable in distilled models and even LoRA adaptations of the same base model. Although attribution weakens as surface and lexical cues are altered, substantial model-specific signal remains. These signals transfer across prompt sources and languages, with semantic representations proving most robust across languages. These findings show that model identity is recoverable at multiple resolutions, but that recoverability depends on model similarity and the evidence available in the text.

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Topics: Paraphrase, Artificial intelligence, Generalization, Natural language processing, Computer science, Similarity (geometry)

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