There have been many efforts to try to understand what grammatical knowledge (e.g., ability to understand the part of speech of a token) is encoded in large pre-trained language models (LM). This is done through `Edge Probing' (EP) tests: simple ML models that predict the grammatical properties of a span (whether it has a particular part of speech) using \textit{only} the LM's token representations. However, most NLP applications use \finetuned\ LMs. Here, we ask: if a LM is \finetuned, does the encoding of linguistic information in it change, as measured by EP tests? Conducting experiments on multiple question-answering (QA) datasets, we answer that question negatively: the EP test results do not change significantly when the fine-tuned QA model performs well or in adversarial situations where the model is forced to learn wrong correlations. However, a critical analysis of the EP task datasets reveals that EP models may rely on spurious correlations to make predictions. This indicates even if \finetuning\ changes the encoding of such knowledge, the EP tests might fail to measure it.

通过对语言模型进行边缘探测的方法，研究发现，即使是对语言模型进行针对性微调，在边缘探测结果中对于语言的编码并没有显著的变化，推测是因为边缘探测本身容易受到数据集的偏见影响，进行数据偏见校正后可以得到更好的结果。

边缘探测任务能揭示问答模型的语言知识吗？