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This paper presents a five-stage framework integrating large language models into survey research, addressing declining response rates, sample bias, and fraudulent completions. Using 2024 Hurricane Milton survey data, the authors propose a theory-informed LLM (A-TLM) that outperforms classical imputation methods in missing-data scenarios and demonstrates manageable hallucination risk through grounded refusal.