@inproceedings{zhang-etal-2026-probe,
    title = "{PROBE}: {PRO}cess-Based {BE}nchmark for Hallucination Detection",
    author = "Zhang, Yu  and
      Belcak, Peter  and
      Diao, Shizhe  and
      Fu, Yonggan  and
      Ghosh, Shaona  and
      Mardani, Morteza  and
      Long, Eileen Margaret Peters  and
      Yu, Bei  and
      Molchanov, Pavlo",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.2099/",
    pages = "42303--42320",
    ISBN = "979-8-89176-395-1",
    abstract = "Hallucination detection remains a significant challenge for large language models. Existing agentic applications rely on LLMs to self-assess the factuality of their outputs using single-step ``LLM-as-a-judge'' prompts. However, even when equipped with ground truth information, current LLMs still fall short in detecting hallucinations, and this one-shot evaluation offers neither the transparency nor the granularity needed to diagnose where and why the detection fails. To address this gap, we introduce PROBE (Process-based Benchmark for Hallucination Detection), a comprehensive benchmark that breaks down hallucination detection into four critical steps: claim decomposition, evidence finding, evidence evaluation, and hallucination localization, and evaluates each step individually. PROBE consists of 12,000 test cases across three task types{---}summarization, question answering, and style transfer. Critically, we demonstrate that when hallucination detection is treated as a multi-step process, all models achieve considerably better performance. Through extensive evaluation, we show that current LLMs struggle chiefly with evidence finding, and that finetuning on our released training data substantially improves performance on this step. PROBE represents a significant step toward more transparent, diagnosable, and robust hallucination detection systems."
}