Scaling Retrieval-Based Language Models with a Trillion-Token Datastore

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This paper explores how increasing datastore size enhances retrieval-based language models' performance, demonstrating that smaller models with large datastores outperform larger models in knowledge-intensive tasks.

  1. Arxiv Papers
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