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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
Outline
KAG-Thinker is a model that upgrades KAG to a multi-turn interactive thinking and deep reasoning framework based on a lightweight parameterized large-scale language model (LLM). It builds a structured thinking process for solving complex problems, and improves the logical consistency and contextual consistency of the reasoning process in question-and-answer (Q&A) tasks on a specific domain knowledge base (KB). 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. The dependencies and parameter passing between these tasks are explicitly modeled through the logical function interface. The search function retrieves the first-level structured and unstructured information of a given knowledge unit. The mathematical and deductive functions are used for inference analysis tasks. In the knowledge retrieval subproblem task, the LLM and external knowledge sources are considered as equivalent KBs. It uses a knowledge boundary module that determines the optimal source using self-regulating mechanisms such as confidence correction and reflective reasoning, and a depth solving module that improves the comprehensiveness of knowledge acquisition.
Takeaways, Limitations
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Takeaways:
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Presenting an efficient multi-turn interaction inference framework based on lightweight LLM
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Improving logical consistency and contextual consistency of the reasoning process through a structural thinking process based on logical format.
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Clear separation and efficient linkage of knowledge retrieval and inference analysis tasks.
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Integrated use of LLM and external knowledge sources and selection of optimal sources
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Increasing the comprehensiveness of knowledge acquisition through in-depth solution modules
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Limitations:
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The paper does not present specific performance evaluation results or comparative analysis.
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Further validation of the efficiency and accuracy of the decomposition process for complex questions is needed.
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Lack of detailed description of the specific mechanisms and performance of the knowledge boundary module and deep resolution module.
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Further research is needed on generalizability to questions of different domains and complexity.
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Lack of a detailed algorithmic description of breadth decomposition.
