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Beyond Text: Optimizing RAG with Multimodal Inputs for Industrial Applications
Summary
This research paper investigates the effectiveness of incorporating images alongside text in Retrieval Augmented Generation (RAG) systems for industrial applications. The authors explore two approaches for integrating multimodal models into RAG systems: using multimodal embeddings and generating textual summaries from images. The study compares the performance of these approaches with single-modality RAG systems and a baseline model that does not utilize any retrieval. They evaluate the performance of each configuration using six metrics, including answer correctness, answer relevance, and faithfulness to both text and image content. The results indicate that multimodal RAG can outperform single-modality RAG, but image retrieval poses significant challenges. The paper concludes that leveraging textual summaries from images presents a more promising approach compared to multimodal embeddings.
原文链接：https://arxiv.org/abs/2410.21943
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