FilterRAG: 零样本引导式检索增强生成以缓解视觉问答中的幻觉
FilterRAG: Zero-Shot Informed Retrieval-Augmented Generation to Mitigate Hallucinations in VQA
AI总结：
FilterRAG通过结合BLIP-VQA与检索增强生成，利用外部知识源减少视觉问答中的幻觉问题，提升模型在知识驱动和分布外场景的鲁棒性。
摘要：
视觉问答需要模型通过整合视觉和文本理解生成准确答案。然而，VQA模型仍然在幻觉问题上存在困难，产生有说服力但错误的答案，特别是在知识驱动和分布外场景中。我们引入了FilterRAG，一种结合BLIP-VQA与检索增强生成的检索增强框架，通过外部知识源如维基百科和DBpedia来支撑答案。FilterRAG在OK-VQA数据集上实现了36.5%的准确率，证明了其在域内和分布外设置中减少幻觉和提高鲁棒性的有效性。这些发现突显了FilterRAG在改进视觉问答系统以适应现实部署中的潜力。
英文摘要：
Visual Question Answering requires models to generate accurate answers by integrating visual and textual understanding. However, VQA models still struggle with hallucinations, producing convincing but incorrect answers, particularly in knowledge-driven and Out-of-Distribution scenarios. We introduce FilterRAG, a retrieval-augmented framework that combines BLIP-VQA with Retrieval-Augmented Generation to ground answers in external knowledge sources like Wikipedia and DBpedia. FilterRAG achieves 36.5% accuracy on the OK-VQA dataset, demonstrating its effectiveness in reducing hallucinations and improving robustness in both in-domain and Out-of-Distribution settings. These findings highlight the potential of FilterRAG to improve Visual Question Answering systems for real-world deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
