@inproceedings{devasier-etal-2025-llms,
title = "Can {LLM}s Extract Frame-Semantic Arguments?",
author = "Devasier, Jacob and
Mediratta, Rishabh and
Li, Chengkai",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1557/",
doi = "10.18653/v1/2025.emnlp-main.1557",
pages = "30609--30622",
ISBN = "979-8-89176-332-6",
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 72B 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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<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 72B 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.</abstract>
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%0 Conference Proceedings
%T Can LLMs Extract Frame-Semantic Arguments?
%A Devasier, Jacob
%A Mediratta, Rishabh
%A Li, Chengkai
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F devasier-etal-2025-llms
%X 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 72B 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.
%R 10.18653/v1/2025.emnlp-main.1557
%U https://aclanthology.org/2025.emnlp-main.1557/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1557
%P 30609-30622
Markdown (Informal)
[Can LLMs Extract Frame-Semantic Arguments?](https://aclanthology.org/2025.emnlp-main.1557/) (Devasier et al., EMNLP 2025)
ACL
- Jacob Devasier, Rishabh Mediratta, and Chengkai Li. 2025. Can LLMs Extract Frame-Semantic Arguments?. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 30609–30622, Suzhou, China. Association for Computational Linguistics.