Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models
James A. Michaelov, Catherine Arnett, Tyler A. Chang, Ben Bergen
Abstract
grammatical knowledge—of parts of speech and grammatical patterns—is key to the capacity for linguistic generalization in humans. But how abstract is grammatical knowledge in large language models? In the human literature, compelling evidence for grammatical abstraction comes from structural priming. A sentence that shares the same grammatical structure as a preceding sentence is processed and produced more readily. Because confounds exist when using stimuli in a single language, evidence of abstraction is even more compelling from crosslingual structural priming, where use of a syntactic structure in one language primes an analogous structure in another language. We measure crosslingual structural priming in large language models, comparing model behavior to human experimental results from eight crosslingual experiments covering six languages, and four monolingual structural priming experiments in three non-English languages. We find evidence for abstract monolingual and crosslingual grammatical representations in the models that function similarly to those found in humans. These results demonstrate that grammatical representations in multilingual language models are not only similar across languages, but they can causally influence text produced in different languages.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 97caaf74-8a8f-4940-a98c-69196ab7262eCited by top-tier papers3
- What Makes a Good Natural Language Prompt?Do Xuan Long, Duy Dinh, Ngoc-Hai Nguyen, Kenji Kawaguchi et al.ACL 2025 · 13 citations
- Different types of syntactic agreement recruit the same units within large language modelsDaria Kryvosheieva, Andrea Gregor de Varda, Evelina Fedorenko, Greta TuckuteACL 2026 · 3 citations
- On the Acquisition of Shared Grammatical Representations in Bilingual Language ModelsCatherine Arnett, Tyler A. Chang, James A. Michaelov, Ben BergenACL 2025
Builds on21
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah et al.NeurIPS 2020 · 64,255 citations
- The Curious Case of Neural Text DegenerationAri Holtzman, Jan Buys, Li Du, Maxwell Forbes et al.ICLR 2020 · 4,112 citations
- An empirical analysis of compute-optimal large language model trainingJordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya et al.NeurIPS 2022 · 566 citations
- Unsupervised Cross-lingual Representation Learning at ScaleAlexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary et al.ACL 2020 · 539 citations
- Cross-Lingual Ability of Multilingual BERT: An Empirical StudyKarthikeyan K, Zihan Wang, Stephen Mayhew, Dan RothICLR 2020 · 378 citations
Related papers
- Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-TuningForrest Davis, Marten van SchijndelACL 2021
- Multilingual AMR-to-Text GenerationAngela Fan, Claire GardentEMNLP 2020 · 25 citations
- The Same but Different: Structural Similarities and Differences in Multilingual Language ModelingRuochen Zhang, Qinan Yu, Matianyu Zang, Carsten Eickhoff et al.ICLR 2025
- Structural Guidance for Transformer Language ModelsPeng Qian, Tahira Naseem, Roger Levy, Ramón Fernandez AstudilloACL 2021
- Prompting is not a substitute for probability measurements in large language modelsJennifer Hu, Roger LevyEMNLP 2023 · 31 citations
