@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://preview.aclanthology.org/transition-to-people-yaml/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."
}