@inproceedings{mohammadi-etal-2025-llms,
title = "Do {LLM}s Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions",
author = "Mohammadi, Seyedali and
Vedula, Bhaskara Hanuma and
Lamba, Hemank and
Raff, Edward and
Kumaraguru, Ponnurangam and
Ferraro, Francis and
Gaur, Manas",
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.1648/",
pages = "32368--32381",
ISBN = "979-8-89176-332-6",
abstract = "Do LLMs genuinely incorporate external definitions, or do they primarily rely on their parametric knowledge? To address these questions, we conduct controlled experiments across multiple explanation benchmark datasets (general and domain-specific) and label definition conditions, including expert-curated, LLM-generated, perturbed, and swapped definitions. Our results reveal that while explicit label definitions can enhance accuracy and explainability, their integration into an LLM{'}s task-solving processes is neither guaranteed nor consistent, suggesting reliance on internalized representations in many cases. Models often default to their internal representations, particularly in general tasks, whereas domain-specific tasks benefit more from explicit definitions. These findings underscore the need for a deeper understanding of how LLMs process external knowledge alongside their pre-existing capabilities."
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%0 Conference Proceedings
%T Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions
%A Mohammadi, Seyedali
%A Vedula, Bhaskara Hanuma
%A Lamba, Hemank
%A Raff, Edward
%A Kumaraguru, Ponnurangam
%A Ferraro, Francis
%A Gaur, Manas
%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 mohammadi-etal-2025-llms
%X Do LLMs genuinely incorporate external definitions, or do they primarily rely on their parametric knowledge? To address these questions, we conduct controlled experiments across multiple explanation benchmark datasets (general and domain-specific) and label definition conditions, including expert-curated, LLM-generated, perturbed, and swapped definitions. Our results reveal that while explicit label definitions can enhance accuracy and explainability, their integration into an LLM’s task-solving processes is neither guaranteed nor consistent, suggesting reliance on internalized representations in many cases. Models often default to their internal representations, particularly in general tasks, whereas domain-specific tasks benefit more from explicit definitions. These findings underscore the need for a deeper understanding of how LLMs process external knowledge alongside their pre-existing capabilities.
%U https://aclanthology.org/2025.emnlp-main.1648/
%P 32368-32381
Markdown (Informal)
[Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions](https://aclanthology.org/2025.emnlp-main.1648/) (Mohammadi et al., EMNLP 2025)
ACL