Probing neural models for the ability to perform downstream tasks using their activation patterns is often used to localize what parts of the network specialize in performing what tasks. However, little work addressed potential mediating factors in such comparisons. As a test-case mediating factor, we consider the prediction’s context length, namely the length of the span whose processing is minimally required to perform the prediction. We show that not controlling for context length may lead to contradictory conclusions as to the localization patterns of the network, depending on the distribution of the probing dataset. Indeed, when probing BERT with seven tasks, we find that it is possible to get 196 different rankings between them when manipulating the distribution of context lengths in the probing dataset. We conclude by presenting best practices for conducting such comparisons in the future.
@inproceedings{DBLP:conf/naacl/SlobodkinCA21,
author = {Aviv Slobodkin and
Leshem Choshen and
Omri Abend},
editor = {Kristina Toutanova and
Anna Rumshisky and
Luke Zettlemoyer and
Dilek Hakkani{-}T{"{u}}r and
Iz Beltagy and
Steven Bethard and
Ryan Cotterell and
Tanmoy Chakraborty and
Yichao Zhou},
title = {Mediators in Determining what Processing {BERT} Performs First},
booktitle = {Proceedings of the 2021 Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language Technologies,
{NAACL-HLT} 2021, Online, June 6-11, 2021},
pages = {86--93},
publisher = {Association for Computational Linguistics},
year = {2021},
url = {https://doi.org/10.18653/v1/2021.naacl-main.8},
doi = {10.18653/V1/2021.NAACL-MAIN.8},
timestamp = {Fri, 06 Aug 2021 01:00:00 +0200},
biburl = {https://dblp.org/rec/conf/naacl/SlobodkinCA21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
See the full reference list in the paper.