Alternative Language Models
Francesco ChiaramonteFrancesco Chiaramonte
Home   >   DeepMind RETRO

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.

User objects:

– Researchers

– Data scientists

– Academics

– Content creators

– Question-answering system developers

– Knowledge-base curators

– AI enthusiasts

– Educators

– Students seeking information

– Tech industry professionals

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Francesco Chiaramonte

Francesco Chiaramonte is renowned for over 10 years of experience, from machine learning to AI entrepreneurship. He shares knowledge and is committed to advancing artificial intelligence, hoping that AI will drive societal progress.

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