Subject Verb Agreement Error Patterns in Meaningless Sentences: Humans vs. BERT
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Abstract (arXiv)
Both humans and neural language models are able to perform subject-verb number agreement (SVA). In principle, semantics shouldn't interfere with this task, which only requires syntactic knowledge. In this work we test whether meaning interferes with this type of agreement in English in syntactic structures of various complexities. To do so, we generate both semantically well-formed and nonsensical items. We compare the performance of BERT-base to that of humans, obtained with a psycholinguistic online crowdsourcing experiment. We find that BERT and humans are both sensitive to our semantic manipulation: They fail more often when presented with nonsensical items, especially when their syntactic structure features an attractor (a noun phrase between the subject and the verb that has not the same number as the subject). We also find that the effect of meaningfulness on SVA errors is stronger for BERT than for humans, showing higher lexical sensitivity of the former on this task.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 21 Sep 2022.
Builds on
9 of 27 references.
References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2016Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependenciescited by 350
- 2019What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language modelscited by 317
- 2018Targeted Syntactic Evaluation of Language Modelscited by 239
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- 2019Assessing BERT's Syntactic Abilitiescited by 191
- 2019Linguistic generalization and compositionality in modern artificial neural networkscited by 58
- 2018Distinct patterns of syntactic agreement errors in recurrent networks and humanscited by 13
- 2022Does BERT really agree ? Fine-grained Analysis of Lexical Dependence on a Syntactic Taskcited by 13
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Citing papers per year: 2023: 3; 2024: 0; 2025: 0; 2026: 3.
6 papers.
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- 2023Multilingual Nonce Dependency Treebanks: Understanding how Language Models represent and process syntactic structurecited by 5
- 2026The Grammar of Transformers: A Systematic Review of Interpretability Research on Syntactic Knowledge in Language Modelscited by 1
- 2026On the Interpretability of Whisper Encodings Using Sparse Autoencoderscited by 1
- 2026The Holistic Storage of Verb+Up Phrases in Text-based and Audio-based Language Modelscited by 0
- 2026The Holistic Storage of Verb+Up Phrases in Text-based and Audio-based Language Models12 Jun 2026
- 2026On the Interpretability of Whisper Encodings Using Sparse Autoencoders12 May 2026
- 2026The Grammar of Transformers: A Systematic Review of Interpretability Research on Syntactic Knowledge in Language Models9 Jan 2026
- 2023Multilingual Nonce Dependency Treebanks: Understanding how Language Models represent and process syntactic structure13 Nov 2023
- 2023Language Model Behavior: A Comprehensive Survey20 Mar 2023
- 2023Dissociating language and thought in large language models16 Jan 2023