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Frame-Semantic Knowledge Injection for Event-Level Inference in LLMs
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:44
摘要
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.
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记录 #1 · 2026-07 · 最新观测版本
Frame-Semantic Knowledge Injection for Event-Level Inference in LLMs
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.
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