DeepMind’s RETRO language model retrieves data from a 2 trillion token database to improve performance. RETRO is more efficient and effective because it uses relevant document chunks in the database that match its input instead of its internal parameters. RETRO performs similarly to GPT-3 and Jurassic-1 on the Pile benchmark despite having 25 times fewer parameters. RETRO excels at skill-intensive tasks like question answering after fine-tuning due to its efficient retrieval mechanism.
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