REVEAL: Retrieval-augmented Visual-language Pre-training With Multi-source Multimodal Knowledge Memory · The Large Language Model Bible Contribute to LLM-Bible

REVEAL: Retrieval-augmented Visual-language Pre-training With Multi-source Multimodal Knowledge Memory

Hu Ziniu, Iscen Ahmet, Sun Chen, Wang Zirui, Chang Kai-wei, Sun Yizhou, Schmid Cordelia, Ross David A., Fathi Alireza. Arxiv 2022

[Paper]    
Applications Multimodal Models RAG Reinforcement Learning Training Techniques

In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. REVEAL consists of four key components: the memory, the encoder, the retriever and the generator. The large-scale memory encodes various sources of multimodal world knowledge (e.g. image-text pairs, question answering pairs, knowledge graph triplets, etc) via a unified encoder. The retriever finds the most relevant knowledge entries in the memory, and the generator fuses the retrieved knowledge with the input query to produce the output. A key novelty in our approach is that the memory, encoder, retriever and generator are all pre-trained end-to-end on a massive amount of data. Furthermore, our approach can use a diverse set of multimodal knowledge sources, which is shown to result in significant gains. We show that REVEAL achieves state-of-the-art results on visual question answering and image captioning.

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