Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟴𝟲 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗘𝗧𝗥𝗢:𝗜𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗯𝘆 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝘁𝗿𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗼𝗳 𝘁𝗼𝗸𝗲𝗻𝘀 𝗯𝘆 𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱 Follow me for a similar post: @🇮🇳 Ashish Patel  ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗥𝗘𝗧𝗥𝗢:𝗜𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗯𝘆 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝘁𝗿𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗼𝗳 𝘁𝗼𝗸𝗲𝗻𝘀 𝗯𝘆 𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱 🔸 This paper is published #arxiv2022. 🔸 DeepMind Retrieval-Enhanced Transformers (Retro), a method for modelling arbitrary text sequences whilst retrieving from databases with trillions of tokens—scaling the data available to models by an order of magnitude compared to what is typically consumed during training. Retro models gains do not diminish for models with up to at least 7B parameters, and correspond to non-retrieval models with 10× more parameters on certain datasets. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens.  🔸 With a 2 trillion token database, our Retrieval-Enhanced Transformer (Retro) obtains comparable performance to GPT-3 and Jurassic-1 on the Pile, despite using 25× fewer parameters.  🔸 After fine-tuning, Retro performance translates to downstream knowledge-intensive tasks such as question answering.  🔸 Retro combines a frozen Bert retriever, a differentiable encoder and a chunked cross-attention mechanism to predict tokens based on an order of magnitude more data than what is typically consumed during training.  🔸 We typically train Retro from scratch, yet can also rapidly Retrofit pre-trained transformers with retrieval and still achieve good performance. Our work opens up new avenues for improving language models through explicit memory at an unprecedented scale. #computervision #artificialintelligence #innovation