Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models
James A. Michaelov, Catherine Arnett, Tyler A. Chang, Ben Bergen
摘要
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.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper3
- What Makes a Good Natural Language Prompt?Do Xuan Long, Duy Dinh, Ngoc-Hai Nguyen, Kenji Kawaguchi 等ACL 2025 · 被引用 13 次
- Different types of syntactic agreement recruit the same units within large language modelsDaria Kryvosheieva, Andrea Gregor de Varda, Evelina Fedorenko, Greta TuckuteACL 2026 · 被引用 3 次
- On the Acquisition of Shared Grammatical Representations in Bilingual Language ModelsCatherine Arnett, Tyler A. Chang, James A. Michaelov, Ben BergenACL 2025
它引用的顶会 Paper21
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah 等NeurIPS 2020 · 被引用 64,255 次
- The Curious Case of Neural Text DegenerationAri Holtzman, Jan Buys, Li Du, Maxwell Forbes 等ICLR 2020 · 被引用 4,112 次
- An empirical analysis of compute-optimal large language model trainingJordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya 等NeurIPS 2022 · 被引用 566 次
- Unsupervised Cross-lingual Representation Learning at ScaleAlexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary 等ACL 2020 · 被引用 539 次
- Cross-Lingual Ability of Multilingual BERT: An Empirical StudyKarthikeyan K, Zihan Wang, Stephen Mayhew, Dan RothICLR 2020 · 被引用 378 次
相关 Paper
- 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 · 被引用 25 次
- The Same but Different: Structural Similarities and Differences in Multilingual Language ModelingRuochen Zhang, Qinan Yu, Matianyu Zang, Carsten Eickhoff 等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 · 被引用 31 次
