@inproceedings{guo-etal-2024-nutframe-frame,
title = "{N}ut{F}rame: Frame-based Conceptual Structure Induction with {LLM}s",
author = "Guo, Shaoru and
Chen, Yubo and
Liu, Kang and
Li, Ru and
Zhao, Jun",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1079",
pages = "12330--12335",
abstract = "Conceptual structure is fundamental to human cognition and natural language understanding. It is significant to explore whether Large Language Models (LLMs) understand such knowledge. Since FrameNet serves as a well-defined conceptual structure knowledge resource, with meaningful frames, fine-grained frame elements, and rich frame relations, we construct a benchmark for coNceptual structure induction based on FrameNet, called NutFrame. It contains three sub-tasks: Frame Induction, Frame Element Induction, and Frame Relation Induction. In addition, we utilize prompts to induce conceptual structure of Framenet with LLMs. Furthermore, we conduct extensive experiments on NutFrame to evaluate various widely-used LLMs. Experimental results demonstrate that FrameNet induction remains a challenge for LLMs.",
}
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<abstract>Conceptual structure is fundamental to human cognition and natural language understanding. It is significant to explore whether Large Language Models (LLMs) understand such knowledge. Since FrameNet serves as a well-defined conceptual structure knowledge resource, with meaningful frames, fine-grained frame elements, and rich frame relations, we construct a benchmark for coNceptual structure induction based on FrameNet, called NutFrame. It contains three sub-tasks: Frame Induction, Frame Element Induction, and Frame Relation Induction. In addition, we utilize prompts to induce conceptual structure of Framenet with LLMs. Furthermore, we conduct extensive experiments on NutFrame to evaluate various widely-used LLMs. Experimental results demonstrate that FrameNet induction remains a challenge for LLMs.</abstract>
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%0 Conference Proceedings
%T NutFrame: Frame-based Conceptual Structure Induction with LLMs
%A Guo, Shaoru
%A Chen, Yubo
%A Liu, Kang
%A Li, Ru
%A Zhao, Jun
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F guo-etal-2024-nutframe-frame
%X Conceptual structure is fundamental to human cognition and natural language understanding. It is significant to explore whether Large Language Models (LLMs) understand such knowledge. Since FrameNet serves as a well-defined conceptual structure knowledge resource, with meaningful frames, fine-grained frame elements, and rich frame relations, we construct a benchmark for coNceptual structure induction based on FrameNet, called NutFrame. It contains three sub-tasks: Frame Induction, Frame Element Induction, and Frame Relation Induction. In addition, we utilize prompts to induce conceptual structure of Framenet with LLMs. Furthermore, we conduct extensive experiments on NutFrame to evaluate various widely-used LLMs. Experimental results demonstrate that FrameNet induction remains a challenge for LLMs.
%U https://aclanthology.org/2024.lrec-main.1079
%P 12330-12335
Markdown (Informal)
[NutFrame: Frame-based Conceptual Structure Induction with LLMs](https://aclanthology.org/2024.lrec-main.1079) (Guo et al., LREC-COLING 2024)
ACL
- Shaoru Guo, Yubo Chen, Kang Liu, Ru Li, and Jun Zhao. 2024. NutFrame: Frame-based Conceptual Structure Induction with LLMs. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12330–12335, Torino, Italia. ELRA and ICCL.