Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts

• URL: http://arxiv.org/abs/2502.17297v1
• Date: Mon, 24 Feb 2025 16:25:25 GMT
• Title: Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts
• Authors: Zhenghao Liu, Xingsheng Zhu, Tianshuo Zhou, Xinyi Zhang, Xiaoyuan Yi, Yukun Yan, Yu Gu, Ge Yu, Maosong Sun,
• Abstract summary: This paper introduces Multi-Modal Retrieval-Augmented Generation (M2RAG)<n>M2RAG is a benchmark designed to evaluate the effectiveness of Multi-modal Large Language Models (MLLMs)<n>To enhance the context utilization capabilities of MLLMs, we also introduce Multi-Modal Retrieval-Augmented Instruction Tuning (MM-RAIT)
• Score: 56.30364248231053
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces Multi-Modal Retrieval-Augmented Generation (M^2RAG), a benchmark designed to evaluate the effectiveness of Multi-modal Large Language Models (MLLMs) in leveraging knowledge from multi-modal retrieval documents. The benchmark comprises four tasks: image captioning, multi-modal question answering, multi-modal fact verification, and image reranking. All tasks are set in an open-domain setting, requiring RAG models to retrieve query-relevant information from a multi-modal document collection and use it as input context for RAG modeling. To enhance the context utilization capabilities of MLLMs, we also introduce Multi-Modal Retrieval-Augmented Instruction Tuning (MM-RAIT), an instruction tuning method that optimizes MLLMs within multi-modal contexts. Our experiments show that MM-RAIT improves the performance of RAG systems by enabling them to effectively learn from multi-modal contexts. All data and code are available at https://github.com/NEUIR/M2RAG.

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