Abstract The last decade has yielded dramatic and quite surprising breakthroughs in natural language processing through the use of simple artificial neural network computations, replicated on a very large scale and trained over exceedingly large amounts of data. The resulting pretrained language models, such as BERT and GPT-3, have provided a powerful universal language understanding and generation base, which can easily be adapted to many understanding, writing, and reasoning tasks. These models show the first inklings of a more general form of artificial intelligence, which may lead to powerful foundation models in domains of sensory experience beyond just language.

中文翻译：

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人类语言理解与推理

摘要 过去十年，通过使用简单的人工神经网络计算，在自然语言处理方面取得了巨大而令人惊讶的突破，大规模复制并在大量数据上进行了训练。由此产生的预训练语言模型，例如 BERT 和 GPT-3，提供了强大的通用语言理解和生成基础，可以轻松适应许多理解、写作和推理任务。这些模型展示了一种更普遍的人工智能形式的初步迹象，这可能会在感官体验领域产生强大的基础模型，而不仅仅是语言。