Prompting is now a dominant method for evaluating the linguistic knowledge of large language models (LLMs). While other methods directly read out models' probability distributions over strings, prompting requires models to access this internal information by processing linguistic input, thereby implicitly testing a new type of emergent ability: metalinguistic judgment. In this study, we compare metalinguistic prompting and direct probability measurements as ways of measuring models' knowledge of English. Broadly, we find that LLMs' metalinguistic judgments are inferior to quantities directly derived from representations. Furthermore, consistency gets worse as the prompt diverges from direct measurements of next-word probabilities. Our findings suggest that negative results relying on metalinguistic prompts cannot be taken as conclusive evidence that an LLM lacks a particular linguistic competence. Our results also highlight the lost value with the move to closed APIs where access to probability distributions is limited.

翻译：提示（Prompting）现在是评估大语言模型（LLMs）语言知识的主流方法。与直接读取模型在字符串上的概率分布的其他方法不同，提示要求模型通过处理语言输入来访问这些内部信息，从而隐式测试一种新型的涌现能力：元语言判断。在本研究中，我们比较了元语言提示和直接概率测量这两种评估模型英语知识的方法。总体而言，我们发现LLMs的元语言判断能力弱于从表征中直接推导出的量化指标。此外，当提示偏离对下一个词概率的直接测量时，一致性会变得更差。我们的研究结果表明，依赖元语言提示的负面结果不能作为LLMs缺乏特定语言能力的确定性证据。我们的结果也凸显了在转向访问概率分布受限的封闭API时所损失的价值。