@inproceedings{wang-etal-2025-task,
title = "Re-{TASK}: Revisiting {LLM} Tasks from Capability, Skill, and Knowledge Perspectives",
author = "Wang, Zhihu and
Zhao, Shiwan and
Wang, Yu and
Huang, Heyuan and
Xie, Sitao and
Zhang, Yubo and
Shi, Jiaxin and
Wang, Zhixing and
Li, Hongyan and
Yan, Junchi",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.254/",
doi = "10.18653/v1/2025.findings-acl.254",
pages = "4925--4936",
ISBN = "979-8-89176-256-5",
abstract = "The Chain-of-Thought (CoT) paradigm has become a pivotal method for solving complex problems with large language models (LLMs). However, its application to domain-specific tasks remains challenging, as LLMs often fail to decompose tasks accurately or execute subtasks effectively. This paper introduces the Re-TASK framework, a novel theoretical model that Revisits LLM Tasks from cApability, Skill, and Knowledge perspectives, drawing on the principles of Bloom{'}s Taxonomy and Knowledge Space Theory. While CoT provides a workflow-centric perspective on tasks, Re-TASK introduces a Chain-of-Learning (CoL) paradigm that highlights task dependencies on specific capability items, further broken down into their constituent knowledge and skill components. To address CoT failures, we propose a Re-TASK prompting strategy, which strengthens task-relevant capabilities through targeted knowledge injection and skill adaptation. Experiments across diverse domains demonstrate the effectiveness of Re-TASK. In particular, we achieve improvements of 45.00{\%} on Yi-1.5-9B and 24.50{\%} on Llama3-Chinese-8B for legal tasks. These results highlight the potential of Re-TASK to significantly enhance LLM performance and its applicability in specialized domains. We release our code and data at https://github.com/Uylee/Re-TASK."
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%0 Conference Proceedings
%T Re-TASK: Revisiting LLM Tasks from Capability, Skill, and Knowledge Perspectives
%A Wang, Zhihu
%A Zhao, Shiwan
%A Wang, Yu
%A Huang, Heyuan
%A Xie, Sitao
%A Zhang, Yubo
%A Shi, Jiaxin
%A Wang, Zhixing
%A Li, Hongyan
%A Yan, Junchi
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wang-etal-2025-task
%X The Chain-of-Thought (CoT) paradigm has become a pivotal method for solving complex problems with large language models (LLMs). However, its application to domain-specific tasks remains challenging, as LLMs often fail to decompose tasks accurately or execute subtasks effectively. This paper introduces the Re-TASK framework, a novel theoretical model that Revisits LLM Tasks from cApability, Skill, and Knowledge perspectives, drawing on the principles of Bloom’s Taxonomy and Knowledge Space Theory. While CoT provides a workflow-centric perspective on tasks, Re-TASK introduces a Chain-of-Learning (CoL) paradigm that highlights task dependencies on specific capability items, further broken down into their constituent knowledge and skill components. To address CoT failures, we propose a Re-TASK prompting strategy, which strengthens task-relevant capabilities through targeted knowledge injection and skill adaptation. Experiments across diverse domains demonstrate the effectiveness of Re-TASK. In particular, we achieve improvements of 45.00% on Yi-1.5-9B and 24.50% on Llama3-Chinese-8B for legal tasks. These results highlight the potential of Re-TASK to significantly enhance LLM performance and its applicability in specialized domains. We release our code and data at https://github.com/Uylee/Re-TASK.
%R 10.18653/v1/2025.findings-acl.254
%U https://aclanthology.org/2025.findings-acl.254/
%U https://doi.org/10.18653/v1/2025.findings-acl.254
%P 4925-4936
Markdown (Informal)
[Re-TASK: Revisiting LLM Tasks from Capability, Skill, and Knowledge Perspectives](https://aclanthology.org/2025.findings-acl.254/) (Wang et al., Findings 2025)
ACL
- Zhihu Wang, Shiwan Zhao, Yu Wang, Heyuan Huang, Sitao Xie, Yubo Zhang, Jiaxin Shi, Zhixing Wang, Hongyan Li, and Junchi Yan. 2025. Re-TASK: Revisiting LLM Tasks from Capability, Skill, and Knowledge Perspectives. In Findings of the Association for Computational Linguistics: ACL 2025, pages 4925–4936, Vienna, Austria. Association for Computational Linguistics.