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KAG-Thinker: Interactive Thinking and Deep Reasoning in LLMs via Knowledge-Augmented Generation
Created by
Haebom
Author
Dalong Zhang, Jun Xu, Jun Zhou, Lei Liang, Lin Yuan, Ling Zhong, Mengshu Sun, Peilong Zhao, QiWei Wang, Xiaorui Wang, Xinkai Du, YangYang Hou, Yu Ao, ZhaoYang Wang, Zhengke Gui, ZhiYing Yi, Zhongpu Bo, Haofen Wang, Huajun Chen
Outline
KAG-Thinker is a model that upgrades the existing KAG to a multi-round interactive thinking and deep reasoning framework based on a lightweight large-scale language model (LLM). It constructs a structured thinking process for solving complex problems, and improves logical consistency and contextual consistency in the reasoning process of question-and-answer (Q&A) tasks in domain-specific knowledge bases (KBs). It follows the logical form-based search and reasoning technology path of KAG, and decomposes complex questions into independently solvable subproblems (logical forms) through breadth decomposition. Each logical form is expressed in two equivalent forms: natural language and logical functions, and is classified into knowledge retrieval or inference analysis tasks. It explicitly models dependencies and parameter passing between tasks through the logical function interface. The search function retrieves the first-level structured and unstructured information of the specified knowledge unit, and the mathematical and inference functions perform inference analysis tasks. In the knowledge retrieval subproblem task, LLM and external knowledge sources are considered as equivalent KBs, and the optimal source is determined through the knowledge boundary module using self-regulating mechanisms such as confidence correction and reflective reasoning, and the depth solving module is used to improve the comprehensiveness of knowledge acquisition.
Takeaways, Limitations
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Takeaways:
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We present a framework that enables multi-step interactive thinking and deep reasoning by utilizing lightweight LLM.
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Improved question-and-answer (Q&A) performance with improved logical and contextual consistency.
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Presenting an efficient decomposition and processing process for knowledge retrieval and inference analysis tasks.
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Integrate LLM with external knowledge sources to enhance the comprehensiveness of knowledge acquisition.
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Selection of optimal knowledge sources through self-regulating mechanisms.
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Limitations:
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The paper lacks specific performance evaluation results and information on the models to be compared.
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Further analysis is needed on the effectiveness and limitations of breadth decomposition for solving complex problems .
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Lack of detailed description of the specific operation and performance of the knowledge boundary module and depth solving module.
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Generalization performance verification is needed for questions of various domains and complexity.
