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Multi-modal Retrieval Augmented Generation
Abstract
Large language models (LLMs) have demonstrated remarkable capabilities in text generation. However, incorporating visual context remains a challenge for specialized domains. In this paper, we introduce a novel semantic retrieval framework that aligns cross-modal embeddings...
1. Introduction
The advent of Retrieval-Augmented Generation (RAG) has significantly reduced hallucination rates in text-only models (Lewis et al., 2020). Despite this progress, applying RAG to multi-modal datasets requires careful consideration of vector spaces.
Recent advances in contrastive learning suggest a unified embedding space is optimalIncluded: Literature Review Database
Transcluded from 3 pages • Auto-syncing updates
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The results indicate a significant improvement in baseline metrics...
Actually, we should caveat this with the sample size limitation.
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