论文标题
语言模型了解我们，很差
Language Models Understand Us, Poorly
论文作者
论文摘要
一些主张语言模型了解我们。其他人不会听到。为了澄清，我研究了人类语言理解的三种观点：映射，可靠性和代表。我认为，尽管行为可靠性对于理解是必要的，但内部表示就足够了。他们爬上右山。我审查了最先进的语言和多模式模型：通过表格不足的规范化，它们务实地挑战。我质疑缩放范式：资源限制可能会禁止扩大模型接近理解。最后，我描述了代表如何发展理解科学。我们需要探究内部构建模型，增加更多人类语言的工作，并衡量模型可以学习的内容。
Some claim language models understand us. Others won't hear it. To clarify, I investigate three views of human language understanding: as-mapping, as-reliability and as-representation. I argue that while behavioral reliability is necessary for understanding, internal representations are sufficient; they climb the right hill. I review state-of-the-art language and multi-modal models: they are pragmatically challenged by under-specification of form. I question the Scaling Paradigm: limits on resources may prohibit scaled-up models from approaching understanding. Last, I describe how as-representation advances a science of understanding. We need work which probes model internals, adds more of human language, and measures what models can learn.
