@inproceedings{shen-etal-2025-llms,
title = "Do {LLM}s Know and Understand Domain Conceptual Knowledge?",
author = "Shen, Sijia and
Jiang, Feiyan and
Wang, Peiyan and
Feng, Yubo and
Jiang, Yuchen and
Liu, Chang",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.319/",
doi = "10.18653/v1/2025.findings-emnlp.319",
pages = "5967--5976",
ISBN = "979-8-89176-335-7",
abstract = "This paper focuses on the task of generating concept sememe trees to study whether Large Language Models (LLMs) can understand and generate domain conceptual knowledge. Concept sememe tree is a hierarchical structure that represents lexical meaning by combining sememes and their relationships.To this end, we introduce the Neighbor Semantic Structure (NSS) and Chain-of-Thought (CoT) prompting method to evaluate the effectiveness of various LLMs in generating accurate and comprehensive sememe trees across different domains. The NSS, guided by conceptual metaphors, identifies terms that exhibit significant external systematicity within a hierarchical relational network and incorporates them as examples in the learning process of LLMs. Meanwhile, the CoT prompting method guides LLMs through a systematic analysis of a term{'}s intrinsic core concepts, essential attributes, and semantic relationships, enabling the generation of concept sememe trees.We conduct experiments using datasets drawn from four authoritative terminology manuals and evaluate different LLMs. The experimental results indicate that LLMs possess the capability to capture and represent the conceptual knowledge aspects of domain-specific terms. Moreover, the integration of NSS examples with a structured CoT process allows LLMs to explore domain conceptual knowledge more profoundly, leading to the generation of highly accurate concept sememe trees."
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<abstract>This paper focuses on the task of generating concept sememe trees to study whether Large Language Models (LLMs) can understand and generate domain conceptual knowledge. Concept sememe tree is a hierarchical structure that represents lexical meaning by combining sememes and their relationships.To this end, we introduce the Neighbor Semantic Structure (NSS) and Chain-of-Thought (CoT) prompting method to evaluate the effectiveness of various LLMs in generating accurate and comprehensive sememe trees across different domains. The NSS, guided by conceptual metaphors, identifies terms that exhibit significant external systematicity within a hierarchical relational network and incorporates them as examples in the learning process of LLMs. Meanwhile, the CoT prompting method guides LLMs through a systematic analysis of a term’s intrinsic core concepts, essential attributes, and semantic relationships, enabling the generation of concept sememe trees.We conduct experiments using datasets drawn from four authoritative terminology manuals and evaluate different LLMs. The experimental results indicate that LLMs possess the capability to capture and represent the conceptual knowledge aspects of domain-specific terms. Moreover, the integration of NSS examples with a structured CoT process allows LLMs to explore domain conceptual knowledge more profoundly, leading to the generation of highly accurate concept sememe trees.</abstract>
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%0 Conference Proceedings
%T Do LLMs Know and Understand Domain Conceptual Knowledge?
%A Shen, Sijia
%A Jiang, Feiyan
%A Wang, Peiyan
%A Feng, Yubo
%A Jiang, Yuchen
%A Liu, Chang
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F shen-etal-2025-llms
%X This paper focuses on the task of generating concept sememe trees to study whether Large Language Models (LLMs) can understand and generate domain conceptual knowledge. Concept sememe tree is a hierarchical structure that represents lexical meaning by combining sememes and their relationships.To this end, we introduce the Neighbor Semantic Structure (NSS) and Chain-of-Thought (CoT) prompting method to evaluate the effectiveness of various LLMs in generating accurate and comprehensive sememe trees across different domains. The NSS, guided by conceptual metaphors, identifies terms that exhibit significant external systematicity within a hierarchical relational network and incorporates them as examples in the learning process of LLMs. Meanwhile, the CoT prompting method guides LLMs through a systematic analysis of a term’s intrinsic core concepts, essential attributes, and semantic relationships, enabling the generation of concept sememe trees.We conduct experiments using datasets drawn from four authoritative terminology manuals and evaluate different LLMs. The experimental results indicate that LLMs possess the capability to capture and represent the conceptual knowledge aspects of domain-specific terms. Moreover, the integration of NSS examples with a structured CoT process allows LLMs to explore domain conceptual knowledge more profoundly, leading to the generation of highly accurate concept sememe trees.
%R 10.18653/v1/2025.findings-emnlp.319
%U https://aclanthology.org/2025.findings-emnlp.319/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.319
%P 5967-5976
Markdown (Informal)
[Do LLMs Know and Understand Domain Conceptual Knowledge?](https://aclanthology.org/2025.findings-emnlp.319/) (Shen et al., Findings 2025)
ACL
- Sijia Shen, Feiyan Jiang, Peiyan Wang, Yubo Feng, Yuchen Jiang, and Chang Liu. 2025. Do LLMs Know and Understand Domain Conceptual Knowledge?. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 5967–5976, Suzhou, China. Association for Computational Linguistics.