@inproceedings{mo-etal-2025-cdt,
title = "{CDT}: A Comprehensive Capability Framework for Large Language Models Across Cognition, Domain, and Task",
author = "Mo, Haosi and
Ma, Xinyu and
Liu, Xuebo and
Wong, Derek F. and
Li, Yu and
Liu, Jie and
Zhang, Min",
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.199/",
pages = "3715--3734",
ISBN = "979-8-89176-335-7",
abstract = "Recent advances in Large Language Models (LLMs) have significantly enhanced their capabilities, highlighting the need for comprehensive evaluation frameworks that extend beyond task-specific benchmarks.However, existing benchmarks often focus on isolated abilities, lacking a holistic framework for assessing LLM capabilities.To address this gap, we propose the $\textbf{C}$ognition-$\textbf{D}$omain-$\textbf{T}$ask (CDT) framework, which comprehensively measures a model{'}s capabilities across three dimensions.We expand the scope of model capability definitions at the cognitive level by incorporating the Cattell-Horn-Carroll cognitive theory, refining the categorization of model capabilities.We apply CDT in two directions: dataset capability evaluation and data selection. Experiments show that our capability metrics correlate well with downstream performance and can support effective dataset analysis and construction. The experiments on data selection also show significant improvements in both general and specific benchmarks, achieving scores of 44.3 and 45.4, with an increase of 1.6 and 2.2 points over the baselines, respectively. These results validate the effectiveness and practicality of CDT. Source code and models are available at \url{https://github.com/Alessa-mo/CDT}."
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<abstract>Recent advances in Large Language Models (LLMs) have significantly enhanced their capabilities, highlighting the need for comprehensive evaluation frameworks that extend beyond task-specific benchmarks.However, existing benchmarks often focus on isolated abilities, lacking a holistic framework for assessing LLM capabilities.To address this gap, we propose the Cognition-Domain-Task (CDT) framework, which comprehensively measures a model’s capabilities across three dimensions.We expand the scope of model capability definitions at the cognitive level by incorporating the Cattell-Horn-Carroll cognitive theory, refining the categorization of model capabilities.We apply CDT in two directions: dataset capability evaluation and data selection. Experiments show that our capability metrics correlate well with downstream performance and can support effective dataset analysis and construction. The experiments on data selection also show significant improvements in both general and specific benchmarks, achieving scores of 44.3 and 45.4, with an increase of 1.6 and 2.2 points over the baselines, respectively. These results validate the effectiveness and practicality of CDT. Source code and models are available at https://github.com/Alessa-mo/CDT.</abstract>
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%0 Conference Proceedings
%T CDT: A Comprehensive Capability Framework for Large Language Models Across Cognition, Domain, and Task
%A Mo, Haosi
%A Ma, Xinyu
%A Liu, Xuebo
%A Wong, Derek F.
%A Li, Yu
%A Liu, Jie
%A Zhang, Min
%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 mo-etal-2025-cdt
%X Recent advances in Large Language Models (LLMs) have significantly enhanced their capabilities, highlighting the need for comprehensive evaluation frameworks that extend beyond task-specific benchmarks.However, existing benchmarks often focus on isolated abilities, lacking a holistic framework for assessing LLM capabilities.To address this gap, we propose the Cognition-Domain-Task (CDT) framework, which comprehensively measures a model’s capabilities across three dimensions.We expand the scope of model capability definitions at the cognitive level by incorporating the Cattell-Horn-Carroll cognitive theory, refining the categorization of model capabilities.We apply CDT in two directions: dataset capability evaluation and data selection. Experiments show that our capability metrics correlate well with downstream performance and can support effective dataset analysis and construction. The experiments on data selection also show significant improvements in both general and specific benchmarks, achieving scores of 44.3 and 45.4, with an increase of 1.6 and 2.2 points over the baselines, respectively. These results validate the effectiveness and practicality of CDT. Source code and models are available at https://github.com/Alessa-mo/CDT.
%U https://aclanthology.org/2025.findings-emnlp.199/
%P 3715-3734
Markdown (Informal)
[CDT: A Comprehensive Capability Framework for Large Language Models Across Cognition, Domain, and Task](https://aclanthology.org/2025.findings-emnlp.199/) (Mo et al., Findings 2025)
ACL
- Haosi Mo, Xinyu Ma, Xuebo Liu, Derek F. Wong, Yu Li, Jie Liu, and Min Zhang. 2025. CDT: A Comprehensive Capability Framework for Large Language Models Across Cognition, Domain, and Task. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 3715–3734, Suzhou, China. Association for Computational Linguistics.