Probing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural models. Naive probing studies may have misleading results, but various recent works have suggested more reliable methodologies that compensate for the possible pitfalls of probing. However, these best practices are numerous and fast-evolving. To simplify the process of running a set of probing experiments in line with suggested methodologies, we introduce Probe-Ably: an extendable probing framework which supports and automates the application of probing methods to the user's inputs
翻译:检验(或诊断分类)已成为调查神经模型中是否存在某一组中间特征的流行战略,以调查某一组中间特征是否存在于神经模型的表述中。初步检验研究可能会产生误导性结果,但最近的各种工作提出了更可靠的方法,以弥补可能存在的检验缺陷。然而,这些最佳做法是众多的,而且迅速演变。为了简化按照建议的方法进行一套检验实验的过程,我们引入了一种可扩展的检验框架,用以支持和自动将检验方法应用于用户的投入。