@inproceedings{yadla-2026-syntactic,
    title = "Syntactic Priming in Few-Shot Learning: How Demonstration Structure Shapes {LLM} Performance",
    author = "Yadla, Prasanth",
    editor = "Mohammad, Saif M.  and
      Ousidhoum, Nedjma",
    booktitle = "Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*{SEM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.starsem-conference.2/",
    pages = "13--27",
    ISBN = "979-8-89176-413-2",
    abstract = "Large language models (LLMs) exhibit remarkable few-shot learning capabilities, yet the role of syntactic structure in demonstration examples remains unexplored. Drawing on psycholinguistic research on structural priming, we investigate whether syntactic patterns in few-shot prompts influence LLM outputs and task performance. We conduct systematic experiments across four model families (Llama, Mistral, Qwen, Gemma) using four syntactic constructions (passive voice, cleft sentences, dative alternation, particle placement). Our results reveal robust syntactic priming effects, with priming strength ranging from 1.3{\texttimes} to 6.4{\texttimes} depending on construction type, indicating that models are substantially more likely to produce constructions matching demonstration syntax. Critically, we find that priming strength shows a positive trend with model size (r = 0.85, p = 0.068), with effects intensifying from 7B to 14B parameter models. We demonstrate that priming is construction-specific rather than reflecting general stylistic preferences, and that priming effects persist across multiple intervening sentences. Analysis across three task types (sentence completion, paraphrase generation, story continuation) reveals that syntactic structure in demonstrations influences output style, and that models produce primed constructions even when the task calls for a different syntactic form. These findings have immediate implications for prompt engineering and reveal that LLMs encode syntactic abstractions beyond surface-level pattern matching. We release our benchmark, SyntaxPrime-ICL, containing controlled examples across multiple constructions for evaluating syntactic priming in few-shot contexts."
}