@inproceedings{rai-etal-2026-frame,
    title = "Frame-Semantic Knowledge Injection for Event-Level Inference in {LLM}s",
    author = "Rai, Shahid Iqbal  and
      Croce, Danilo  and
      Basili, Roberto",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-short.55/",
    pages = "664--678",
    ISBN = "979-8-89176-391-3",
    abstract = "Large language models (LLMs) are fluent but often brittle when interpretation depends on external information (e.g., events or participant roles), as next-token prediction does not explicitly encode situation-level semantic constraints. FrameNet provides a structured account of semantics through its inventory of frames, roles, and relations. We present a scalable framework that injects frame-semantic knowledge into LLMs via LoRA, moving from fact-oriented prompting to principle-oriented supervision over the full FrameNet inventory. The supervision encodes semantic constraints through semantic types, sense-aware definitions, frame relations, and role-annotated examples. To test whether this knowledge generalizes beyond surface cues, we use Natural Language Inference (NLI) as a diagnostic task for event-level reasoning. Experiments on CONFER and SNLI show consistent gains over Meta-Llama-3.1-8B-Instruct in zero-shot and few-shot settings, especially for entailment and contradiction. Complementary semantic role labeling analyses further indicate improved sensitivity to frame, role, and span structure."
}