Year: 2025
Type: conference-paper
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.
Authors: Zhihu Wang, Shiwan Zhao, Yu Wang, Heyuan Huang, Sitao Xie, Yubo Zhang, Jiaxin Shi, Zhixing Wang, Hongyan Li, Junchi Yan
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412888734