@article{ghosh-etal-2026-obfusqate,
    title = "{O}bfus{QA}te: A Proposed Framework to Evaluate {LLM} Robustness on Obfuscated Factual Question Answering",
    author = "Ghosh, Shubhra  and
      Borah, Abhilekh  and
      Guru, Aditya Kumar  and
      Ghosh, Kripabandhu",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.401/",
    pages = "5129--5145",
    abstract = "The rapid proliferation of Large Language Models (LLMs) has significantly contributed to the development of equitable AI systems capable of factual question-answering (QA). However, no known study tests the LLMs' robustness when presented with obfuscated versions of questions. To systematically evaluate these limitations, we propose a novel technique, ObfusQAte and leveraging the same, introduce ObfusQA, a comprehensive, first of its kind, framework, with multi-tiered obfuscation levels designed to examine LLM capabilities across three distinct dimensions: (i) Named-Entity Indirection, (ii) Distractor Indirection, and (iii) Contextual Overload. By capturing these fine-grained distinctions in language, ObfusQA provides a comprehensive benchmark for evaluating LLM robustness and adaptability. Our study observes that LLMs exhibit a tendency to fail or generate hallucinated responses, when confronted with these increasingly nuanced variations. To foster research in this direction, we make ObfusQAte publicly available."
}