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DeepMind's Gopher: A Smaller, More Efficient Language Model DeepMind's Gopher is a noteworthy language model that is larger than GPT-3 but more condensed than some extremely large models. With 280 billion parameters, it is able to perform as well as or even better than other models in a variety of specialized tasks, including answering questions about the humanities, mathematics, logical reasoning, and science. According to DeepMind, Gopher can outperform systems that are 25 times bigger. In addition to its impressive performance, Gopher also features a 7 billion-parameter Retro model that matches the performance of GPT-3. This Retro model is unique in that it can trace the precise passage of training text that it used to generate output. This makes it possible to detect bias or false information in the model's responses. Overall, Gopher is a significant breakthrough in the field of language modeling. It is a large-scale language model that performs well while being more efficient and effective than other models. This makes it a valuable tool for researchers and developers who are working on a variety of natural language processing tasks.