@inproceedings{dutta-etal-2026-scene,
    title = "What About the Scene With the {H}itler Reference? {HAUNT}: A Framework to Probe {LLM}s' Self-consistency in Closed Domains Via Adversarial Nudge",
    author = "Dutta, Arka  and
      Dutta, Sujan  and
      Magu, Rijul  and
      Datta, Soumyajit  and
      De Choudhury, Munmun  and
      KhudaBukhsh, Ashiqur R.",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.2169/",
    pages = "46769--46791",
    ISBN = "979-8-89176-390-6",
    abstract = "Hallucinations pose a critical challenge to the real-world deployment of large language models (LLMs) in high-stakes domains. In this paper, we present a framework for stress testing factual fidelity in LLMs in the presence of adversarial nudge. Our framework consists of three steps. First, we instruct the LLM to produce sets of truths and lies consistent with the closed domain in question. Next, we instruct the LLM to verify the same set of assertions as truths and lies consistent with the same closed domain. Finally, we test the robustness of the LLM against the lies generated (and verified) by itself. Our extensive evaluation, conducted using five widely known proprietary and six open LLMs across two closed domains of popular movies and novels, reveals a wide range of susceptibility to adversarial nudges: even among the strongest proprietary LLMs, $\texttt{Claude}$ exhibits strong resilience, $\texttt{GPT}$ and $\texttt{Grok}$ demonstrate moderate resilience, while $\texttt{Gemini}$ and $\texttt{DeepSeek}$ show weak resilience and open models fall short significantly."
}