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.

本文比较了元语言激励和直接概率测量作为衡量英语知识的方法，并发现大语言模型的元语言判断低于直接从表示中派生出的数量。