Michaelov et al. (2023)

Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 3703-3720

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Michaelov, J., Arnett, C., Chang, T., & Bergen, B. (2023). Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 3703-3720. https://doi.org/10.18653/v1/2023.emnlp-main.227

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Priming Memory Systems

Measured in AI

Seven multilingual models (XGLM, 564 million to 7.5 billion parameters, and PolyLM, 1.7 and 13 billion) were run on the materials of 12 human structural priming experiments across six languages, including priming from one language to another. All models showed priming on the single-language materials and most showed it on 8 of the 12 cross-language sets, with larger models usually showing larger effects; the authors compared only the direction of effects with people, not their size