Verb Conjugation in Transformers Is Determined by Linear Encodings of Subject Number
Findings of the Association for Computational Linguistics: EMNLP 2023
Venue: EMNLP
Type: Conference
Causal Intervention
Probing
Interpretability
BERTology
Authors
Affiliations
Sophie Hao
New York University
Tal Linzen
New York University
Google
Abstract
Deep architectures such as Transformers are sometimes criticized for having uninterpretable “black-box” representations. We use causal intervention analysis to show that, in fact, some linguistic features are represented in a linear, interpretable format. Specifically, we show that BERT’s ability to conjugate verbs relies on a linear encoding of subject number that can be manipulated with predictable effects on conjugation accuracy. This encoding is found in the subject position at the first layer and the verb position at the last layer, but distributed across positions at middle layers, particularly when there are multiple cues to subject number.