@inproceedings{gold-etal-2026-veil,
    title = "{VEIL}: A Benchmark for Value-Preserving Entity Identification Limitation",
    author = "Gold, Darina  and
      Rastegar, Shadi  and
      Liebel, Alina  and
      Zarcone, Alessandra",
    editor = {Siegert, Ingo  and
      Szawerna, Maria Irena  and
      Choukri, Khalid  and
      Dobnik, Simon  and
      Kamocki, Pawe{\l}  and
      Lindstr{\"o}m Tiedemann, Therese  and
      Lison, Pierre  and
      Mu{\~n}oz S{\'a}nchez, Ricardo  and
      Pil{\'a}n, Ildik{\'o}  and
      S{\"o}derg{\r{a}}rd, Lisa  and
      Talmoudi, Kossay  and
      Volodina, Elena  and
      Vu, Xuan-Son},
    booktitle = "Proceedings of the Joint Workshop on Legal and Ethical Issues in Human Language Technologies and Computational Approaches to Language Data Pseudonymization, Anonymization, De-identification, and Data Privacy ({LEGAL}2026 and {CALD}-pseudo 2026) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.legal-1.12/",
    doi = "10.63317/2spfi3ghhwaj",
    pages = "102--115",
    abstract = "Large Language Models (LLMs) are linked to several issues regarding Personally Identifiable Information (PII). PII can occur in the training data and can thus be accidentally leaked or extracted with malicious intent, or it can be inputted in LLM-based technologies by users through their prompts. A viable strategy to limit the LLMs' exposure to PII is to filter input and output data by de-identifying PII, including personal names. This however poses a challenge: a name could refer to a private person in a context containing sensitive information (e.g., Michelangelo is an atheist), or it could refer to a famous artist in another context (e.g., Michelangelo{'}s Sistine Chapel), and masking the latter may hinder the LLMs' capabilities in general-knowledge tasks. We tackle the problem of personal name de-identification and focus on the decision of which personal names need to be removed (and which should be kept), based on context. We present VEIL, a challenging benchmark for Value-preserving Entity Identification Limitation, for context-aware de-identification decisions on LLM training data, and compare the performance of different state-of-the-art systems on the task."
}