Google Researchers Propose MEMORY-VQ: A New AI Approach to Reduce Storage Requirements of Memory-Augmented Models without Sacrificing Performance

Recent research in language models has emphasized the importance of retrieval augmentation for enhancing factual knowledge. Retrieval augmentation involves providing these models with relevant text passages to improve their performances, but it comes at a higher computational cost. A new approach, depicted by LUMEN and LUMEN-VQ, aims to speed up the retrieval augmentation by pre-encoding […]

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