Papers with structural priming
Structural Persistence in Language Models: Priming as a Window into Abstract Language Representations (2022.tacl-1)
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| Challenge: | a rich literature has emerged in the last few years addressing these questions, including whether specific LMs have acquired specific linguistic constructions. |
| Approach: | They introduce a novel metric and release Prime-LM, a large corpus where they control for various linguistic factors that interact with priming strength. |
| Outcome: | The proposed model can learn abstract structural information independent of the structure of a sentence and is able to perform tasks that require natural language understanding skills. |
Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models (2023.emnlp-main)
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| Challenge: | Abstract grammatical knowledge is key to linguistic generalization in humans . strong evidence for grammatikal abstraction in humans comes from structural priming . |
| Approach: | They compare human models of crosslingual structural priming to human models . they find evidence for abstract monolingual and crosslingual grammatical representations . |
| Outcome: | The results show that grammatical representations in multilingual models are similar to humans . the strongest evidence for grammatikal abstraction in humans comes from structural priming . |
Is In-Context Learning a Type of Error-Driven Learning? Evidence from the Inverse Frequency Effect in Structural Priming (2025.naacl-long)
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| Challenge: | Recent pre-trained large language models have shown the capacity to perform in-context learning (ICL) this capability could provide a way to bridge the divide between language models and humans. |
| Approach: | They propose a new way of diagnosing whether ICL is error-driven learning . they simulated structural priming with ICL and found the effect was stronger . |
| Outcome: | The proposed method is based on the inverse frequency effect (IFE) phenomenon is similar to error-driven learning in large language models . |
On the Acquisition of Shared Grammatical Representations in Bilingual Language Models (2025.acl-long)
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| Challenge: | Crosslingual transfer is crucial to contemporary language models’ multilingual capabilities, but how it occurs is not well understood. |
| Approach: | They use structural priming to study grammatical representations in humans by controlling for training data quantity and language exposure. |
| Outcome: | The proposed model is able to learn a language in two languages and has a higher likelihood of learning a prepositional object (PO) dative sentence than a double object (DO) . |