2020/09/15 by Zining Zhu, Zhu, Zining, Frank Rudzicz +1 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning in Bioinformatics #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2009.07364
openalex publication_date 2020/09/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
There is increasing interest in assessing the linguistic knowledge encoded in neural representations. A popular approach is to attach a diagnostic classifier -- or "probe" -- to perform supervised classification from internal representations. However, how to select a good probe is in debate. Hewitt and Liang (2019) showed that a high performance on diagnostic classification itself is insufficient, because it can be attributed to either "the representation being rich in knowledge", or "the probe learning the task", which Pimentel et al. (2020) challenged. We show this dichotomy is valid information-theoretically. In addition, we find that the methods to construct and select good probes proposed by the two papers, *control task* (Hewitt and Liang, 2019) and *control function* (Pimentel et al., 2020), are equivalent -- the errors of their approaches are identical (modulo irrelevant terms). Empirically, these two selection criteria lead to results that highly agree with each other.