Can LLMs Extract Frame-Semantic Arguments?

Jacob Devasier; Rishabh Mediratta; Chengkai Li

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arXiv · 2502.12516

Can LLMs Extract Frame-Semantic Arguments?

Jacob Devasier·Rishabh Mediratta·Chengkai Li

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Abstract

Frame-semantic parsing is a critical task in natural language understanding, yet the ability of large language models (LLMs) to extract frame-semantic arguments remains underexplored. This paper presents a comprehensive evaluation of LLMs on frame-semantic argument identification, analyzing the impact of input representation formats, model architectures, and generalization to unseen and out-of-domain samples. Our experiments, spanning models from 0.5B to 78B parameters, reveal that JSON-based representations significantly enhance performance, and while larger models generally perform better, smaller models can achieve competitive results through fine-tuning. We also introduce a novel approach to frame identification leveraging predicted frame elements, achieving state-of-the-art performance on ambiguous targets. Despite strong generalization capabilities, our analysis finds that LLMs still struggle with out-of-domain data.

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Jacob Devasier, Rishabh Mediratta, Chengkai Li. 2025-02-18. Can LLMs Extract Frame-Semantic Arguments?. https://arxiv.org/abs/2502.12516

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