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The zero-shot performance of visual question answering (VQA) models reliesheavily on prompts. For example, a zero-shot VQA for disaster scenarios couldleverage well-designed Chain of Thought (CoT) prompts to stimulate the model'spotential. However, using CoT prompts has some problems, such as causing anincorrect answer in the end due to the hallucination in the thought process. Inthis paper, we propose a zero-shot VQA named Flood Disaster VQA with Two-StagePrompt (VQA-TSP). The model generates the thought process in the first stageand then uses the thought process to generate the final answer in the secondstage. In particular, visual context is added in the second stage to relievethe hallucination problem that exists in the thought process. Experimentalresults show that our method exceeds the performance of state-of-the-artzero-shot VQA models for flood disaster scenarios in total. Our study providesa research basis for improving the performance of CoT-based zero-shot VQA.

Reducing Hallucinations: Enhancing VQA for Flood Disaster Damage Assessment with Visual Contexts