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This research demonstrates that large language models exhibit structural priming during text generation, revealing how syntactic context influences sentence completion across lexical and semantic levels. The findings confirm that LMs do not merely sample randomly but align their output with preceding structural patterns, particularly when semantic coherence is high. This insight into the multi-level alignment mechanisms of generative models advances our understanding of production dynamics in artificial intelligence systems.
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Patterns of Priming in Production: Lexical, Semantic and Structural Alignment in Language Model Generation

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arXiv:2609.04484v1 Announce Type: new Abstract: This paper investigates structural priming in language model (LM) production, examining how preceding structural context influences sentence completion. While prior work has demonstrated priming effects in comprehension of structural alternations, it remained unclear whether these persist in production, where, when generating, an LM samples from many possible continuations at each step. We address this question through a series of controlled sentence-completion experiments on dative constructions. In line with prior work, we find that LMs are susceptible to structural priming, particularly in sentences that are semantically coherent. In terms of priming magnitude, we find that while there is a greater relative increase of double-object datives against our baselines, in line with inverse frequency effects, there is a larger absolute increase in prepositional-objects, the more frequently produced construction. Finally, we not only observe that structural priming is boosted by lexico-semantic coherence, but that structurally primed completions display greater levels of lexico-semantic repetition. Taken together, our evidence supports the view that structural priming in LMs operates across multiple levels of linguistic representation, facilitating, and facilitated by syntactic, lexical, and semantic alignment. Code: https://github.com/the-context-lab/primedproduction.

Abstract:This paper investigates structural priming in language model (LM) production, examining how preceding structural context influences sentence completion. While prior work has demonstrated priming effects in comprehension of structural alternations, it remained unclear whether these persist in production, where, when generating, an LM samples from many possible continuations at each step. We address this question through a series of controlled sentence-completion experiments on dative constructions. In line with prior work, we find that LMs are susceptible to structural priming, particularly in sentences that are semantically coherent. In terms of priming magnitude, we find that while there is a greater relative increase of double-object datives against our baselines, in line with inverse frequency effects, there is a larger absolute increase in prepositional-objects, the more frequently produced construction. Finally, we not only observe that structural priming is boosted by lexico-semantic coherence, but that structurally primed completions display greater levels of lexico-semantic repetition. Taken together, our evidence supports the view that structural priming in LMs operates across multiple levels of linguistic representation, facilitating, and facilitated by syntactic, lexical, and semantic alignment. Code: this https URL.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2609.04484 [cs.CL]

(or arXiv:2609.04484v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.04484 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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