mlsanityData to 7 Oct 2026

Neural reality of argument structure constructions

arXiv 2202.12246 ↗First posted 24 Feb 2022cs.CLcited by 25

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Abstract (arXiv)

In lexicalist linguistic theories, argument structure is assumed to be predictable from the meaning of verbs. As a result, the verb is the primary determinant of the meaning of a clause. In contrast, construction grammarians propose that argument structure is encoded in constructions (or form-meaning pairs) that are distinct from verbs. Decades of psycholinguistic research have produced substantial empirical evidence in favor of the construction view. Here we adapt several psycholinguistic studies to probe for the existence of argument structure constructions (ASCs) in Transformer-based language models (LMs). First, using a sentence sorting experiment, we find that sentences sharing the same construction are closer in embedding space than sentences sharing the same verb. Furthermore, LMs increasingly prefer grouping by construction with more input data, mirroring the behaviour of non-native language learners. Second, in a "Jabberwocky" priming-based experiment, we find that LMs associate ASCs with meaning, even in semantically nonsensical sentences. Our work offers the first evidence for ASCs in LMs and highlights the potential to devise novel probing methods grounded in psycholinguistic research.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 27 of its 63 references that are in the map. First posted on arXiv 24 Feb 2022.

Builds on

27 of 63 references.

References · 27

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
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15 more
  1. 2020When Do You Need Billions of Words of Pretraining Data?cited by 104
  2. 2020Syntactic Structure from Deep Learningcited by 99
  3. 2021Probing Across Time: What Does RoBERTa Know and When?cited by 73
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  12. 2020Spying on your neighbors: Fine-grained probing of contextual embeddings for information about surrounding wordscited by 28
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Citing papers per year: 2022: 2; 2023: 6; 2024: 4; 2025: 8; 2026: 5.

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  1. 2024Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNscited by 65
  2. 2022The Better Your Syntax, the Better Your Semantics? Probing Pretrained Language Models for the English Comparative Correlativecited by 35
  3. 2023A Discerning Several Thousand Judgments: GPT-3 Rates the Article + Adjective + Numeral + Noun Constructioncited by 22
  4. 2023Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Modelscited by 16
  5. 2022Predicting Fine-Tuning Performance with Probingcited by 13
  6. 2023Construction Grammar Provides Unique Insight into Neural Language Modelscited by 13
  7. 2025Linguistic Generalizations are not Rules: Impacts on Evaluation of LMscited by 10
  8. 2025On the Acquisition of Shared Grammatical Representations in Bilingual Language Modelscited by 7
  9. 2025Constructions are Revealed in Word Distributionscited by 6
  10. 2024Hybrid Human-LLM Corpus Construction and LLM Evaluation for Rare Linguistic Phenomenacited by 5
  11. 2025Beyond Memorization: Assessing Semantic Generalization in Large Language Models Using Phrasal Constructionscited by 5
  12. 2025Construction Identification and Disambiguation Using BERT: A Case Study of NPNcited by 4
13 more
  1. 2025Unpacking Let Alone: Human-Scale Models Generalize to a Rare Construction in Form but not Meaningcited by 4
  2. 2023Syntax and Semantics Meet in the "Middle": Probing the Syntax-Semantics Interface of LMs Through Agentivitycited by 3
  3. 2024Analysis of Argument Structure Constructions in the Large Language Model BERTcited by 3
  4. 2023Construction Grammar and Language Modelscited by 2
  5. 2025LLMs Learn Constructions That Humans Do Not Knowcited by 1
  6. 2025Uniform Information Density and Syntactic Reduction: Revisiting textitthat-Mentioning in English Complement Clausescited by 1
  7. 2026Leveraging Lora Fine-Tuning and Knowledge Bases for Construction Identificationcited by 1
  8. 2026Do Language Models Know What Not to Say? Causal Evidence for Statistical Preemption in LLMscited by 1
  9. 2026Developmental approach reveals the statistical learning of Neural Language Models: Transformers generalize from the most abstract statistical patternscited by 1
  10. 2023Crosslingual Structural Priming and the Pre-Training Dynamics of Bilingual Language Modelscited by 0
  11. 2024FinGen: A Dataset for Argument Generation in Financecited by 0
  12. 2026A Hierarchical and Attentional Analysis of Argument Structure Constructions in BERT Using Naturalistic Corporacited by 0
  13. 2026Language Models Learn Constructional Semantics, Not To Mention Syntax: Investigating LM Understanding of Paired-Focus Constructionscited by 0