Re- TASK : Revisiting LLM Tasks from Capability, Skill, and Knowledge Perspectives (2025)

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.

## introduction as the scale of large language models (llms) continues to increase, their general capabilities in natural language processing (nlp) tasks have shown substantial improvements. however, despite these advances, these models often struggle with complex reasoning tasks, particularly those that are domain-specific. the chain-of-thought (cot) technique @xcite @xcite has emerged as a promising paradigm by decomposing complex tasks into a series of subtasks in a divide-and-conquer manner. yet, the application of cot to domain-specific tasks faces significant challenges in both task decomposition @xcite and subtask execution @xcite , due to a lack of domain knowledge and specialized capabilities. the idea that an individual's capabilities directly influence task performance is well supported by educational theories, notably bloom's taxonomy @xcite and knowledge space theory (kst) @xcite . bloom's taxonomy outlines how educational objectives are achieved through structured instructional activities, each involving specific knowledge and cognitive processes. similarly, kst emphasizes the sequential dependencies between learning items, forming "learning pathways" that guide learners from foundational knowledge to mastery. building on these insights into the chain-of-learning (col), we introduce the re-task framework, which revisits llm tasks through the lenses of capability, skill, and knowledge (see figure 1(b) ). this framework posits that the successful completion of tasks foot_0 depends on sequentially mastering multiple capability items, with each item further dissected into its constituent aspects of knowledge and skills. our framework argues that failures in the cot paradigm, particularly in task decomposition and subtask execution, stem from a lack of the necessary capabilities, due to either insufficient knowledge or inadequate knowledge-skill adaptation (abbreviated as skill adaptation). first, llms may lack relevant knowledge due to limited access to proprietary data or issues related to data timeliness. second, even when knowledge is available, llms often struggle to apply it effectively because of inadequate knowledge-skill adaptation, resulting in suboptimal performance. while techniques such as retrieval-augmented generation @xcite can inject knowledge into the context, models may still fail if there is insufficient skill adaptation to effectively utilize the retrieved knowledge. to address these issues, we propose integrating the cot paradigm with the re-task framework to improve llm performance, as shown in figure 1 . specifically, we identify core capability items linked to the overall task and its corresponding subtasks, then strengthen these capabilities through targeted knowledge injection and skill adaptation using a deliberately designed prompting strategy, re-task prompting (see figure 2 ). the capability items represent demonstrations of knowledge-skill adaptation, such as conceptual knowledge understanding and procedural knowledge applying. notably, knowledge itself can be treated as a special capability item, as in the case of knowledge injection through recalling or retrieving. we then adopt in-context learning (icl) techniques @xcite to enhance the corresponding capabilities by carefully arranging these demonstrations within the prompt. we conduct extensive experiments with opensource llms to evaluate the effectiveness of the re-task framework in enhancing cot performance across various domain-specific tasks. specifically, we included datasets from three major domains-finance, law, and stem-comprising a total of five distinct datasets in both chinese and english, along with corresponding multilingual models. this selection ensures that our results are both meaningful and broadly applicable. by incorporating relevant capability items that inject domain knowledge or improve skill adaptation, we observe significant improvements in task performance. additionally, we extend our experiments to llms of different scales, demonstrating that while model capabilities generally improve with scale, the re-task framework continues to enhance performance effectively. our main contributions are summarized as follows: ## experimental results we conducted extensive experiments across the law, finance, mathematics, biology, and physics domains to validate the effectiveness of re-task. ## conclusion in this paper, we introduced the re-task framework, a novel approach that revisits llm tasks through the lenses of capability, skill, and knowledge, aiming to address the limitations of the chainof-thought (cot) paradigm in complex, domainspecific tasks. by integrating re-task with cot, we systematically enhanced the performance of llms through targeted knowledge injection and skill adaptation, using a structured prompting strategy, re-task prompting. our extensive experiments across the law, finance, and stem domains demonstrated that the re-task framework significantly improves task performance, achieving substantial gains over baseline models and confirming the framework's potential to enhance llm capabilities across diverse domains. our research opens new avenues for practitioners to deepen their understanding, evaluation, and improvement of llms.