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arXiv · 2509.19540

Do LLMs Encode Frame Semantics? Evidence from Frame Identification

Jayanth Krishna Chundru·Rudrashis Poddar·Jie Cao·Tianyu Jiang
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Abstract

We investigate whether large language models encode latent knowledge of frame semantics, focusing on frame identification, a core challenge in frame semantic parsing that involves selecting the appropriate semantic frame for a target word in context. Using the FrameNet lexical resource, we evaluate models under prompt-based inference and observe that they can perform frame identification effectively even without explicit supervision. To assess the impact of task-specific training, we fine-tune the model on FrameNet data, which substantially improves in-domain accuracy while generalizing well to out-of-domain benchmarks. Further analysis shows that the models can generate semantically coherent frame definitions, highlighting the model's internalized understanding of frame semantics.

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Jayanth Krishna Chundru, Rudrashis Poddar, Jie Cao, Tianyu Jiang. 2025-09-23. Do LLMs Encode Frame Semantics? Evidence from Frame Identification. https://doi.org/10.18653/v1%2F2025.emnlp-main.1499

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