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They fill forms: JSON fields, function arguments, extraction templates. We show that the form itself causes hallucination.\n  We ask thirteen models the same question about the same input and change only the answer format. The inputs are built so the question cannot be answered: a viral post showing 12,400 likes but no visible replies, a support ticket whose call was never transcribed. In free text, GPT-5.5 says there is no reply data 98% of the time. Given a required JSON field for sentiment, the same model invents an answer 40 times out of 40. It fabricates the mood of crowds it never saw and quotes customers it never heard.\n  Required fields drive fabrication to 100% in ten of thirteen models. An explicit \"insufficient evidence\" option rescues only the frontier: all nine open-weight models ignore it. Under grammar-constrained decoding, where the escape token is guaranteed reac","title":"PhantomFill: When the Form Demands an Answer, Language Models Invent One","url":"https://arxiv.org/abs/2607.20492","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.20492v2 Announce Type: replace-cross \nAbstract: Language models in production do not write prose. They fill forms: JSON fields, function arguments, extraction templates. We show that the form itself causes hallucination.\n  We ask thirteen models the same question about the same input and change only the answer format. The inputs are built so the question cannot be answered: a viral post showing 12,400 likes but no visible replies, a support ticket whose call was never transcribed. In free text, GPT-5.5 says there is no reply data 98% of the time. Given a required JSON field for sentiment, the same model invents an answer 40 times out of 40. It fabricates the mood of crowds it never saw and quotes customers it never heard.\n  Required fields drive fabrication to 100% in ten of thirteen models. An explicit \"insufficient evidence\" option rescues only the frontier: all nine open-weight models ignore it. 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