它称为Gopher的DeepMind的语言模型比许多在许多任务上的现有超大语言模型更为准确,尤其是回答有关科学和人文科学等专业主题的问题,在其他任务中等于或几乎等于它们,例如逻辑推理和数学,根据DeepMind发布的数据。

尽管Gopher比某些超大语言软件小,但事实就是这种情况。 Gopher的参数约为2800亿个,或可以调节的变量。这使其比OpenAI的GPT-3大,该GPT-3具有1750亿。但是,它比微软和Nivida今年早些时候合作的系统小,称为Megatron,具有5350亿,以及由Google构建的系统,具有1.6万亿个参数,而阿里巴巴则为10万亿。

DeepMind说,其新的语言模型可以击败其大小的25倍。使用70亿个参数复古模型,它等于OpenAI的GPT-3的性能。此外,由于研究人员可以准确地看到复古软件用于产生其输出的哪个部分,因此检测偏见或错误信息可能更容易。

资料来源:https://fortune.com/2021/12/08/deepmind-gopher-nlp-ultra-large-lange-model-model-beats-gpt-3/

原文:

DeepMind’s language model, which it calls Gopher, is significantly more accurate than these existing ultra-large language models on many tasks, particularly answering questions about specialized subjects like science and the humanities, and equal or nearly equal to them in others, such as logical reasoning and mathematics, according to the data DeepMind published.

This was the case despite the fact that Gopher is smaller than some ultra-large language software. Gopher has some 280 billion different parameters, or variables that it can tune. That makes it larger than OpenAI’s GPT-3, which has 175 billion. But it is smaller than a system that Microsoft and Nivida collaborated on earlier this year, called Megatron, that has 535 billion, as well as ones constructed by Google, with 1.6 trillion parameters, and Alibaba, with 10 trillion.

DeepMind says its new language model can beat others 25 times its size. With a 7 billion parameter Retro model it equals the performance of OpenAI’s GPT-3. Also, because researchers can see exactly which section of training text the Retro software is using to produce its output, it could be easier to detect bias or misinformation, the DeepMind researchers said.

Source: https://fortune.com/2021/12/08/deepmind-gopher-nlp-ultra-large-language-model-beats-gpt-3/

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