Language models are a rapidly developing field of artificial intelligence with enormous potential to improve our understanding of human cognition. However, many popular language models are cognitively implausible on multiple fronts. For language models to offer plausible insights into human cognitive processing, they should implement a transparent and cognitively plausible learning mechanism, train on a quantity of text that is achievable in a human’s lifetime of language exposure, and not assume to represent all of word meaning. When care is taken to create plausible language models within these constraints, they can be a powerful tool in uncovering the nature and scope of how language shapes semantic knowledge. The distributional relationships between words, which humans represent in memory as linguistic distributional knowledge, allow people to represent and process semantic information flexibly, robustly, and efficiently.

中文翻译：

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语言模型可以告诉我们有关人类认知的哪些信息？

语言模型是人工智能的一个快速发展的领域，具有提高我们对人类认知的理解的巨大潜力。然而，许多流行的语言模型在多个方面在认知上都是不可信的。为了使语言模型能够为人类认知处理提供合理的见解，它们应该实施一种透明且认知上合理的学习机制，对人类一生中接触语言所能达到的文本量进行训练，而不是假设能够代表所有单词含义。当在这些约束下小心创建合理的语言模型时，它们可以成为揭示语言如何塑造语义知识的本质和范围的强大工具。人类在记忆中将单词之间的分布关系表示为语言分布知识，使人们能够灵活、鲁棒、高效地表示和处理语义信息。