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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.10498 |
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| _version_ | 1866915275120574464 |
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| author | Lu, Jianling Lv, Mingqi Chen, Tieming |
| author_facet | Lu, Jianling Lv, Mingqi Chen, Tieming |
| contents | The performance of large language models (LLMs) in Q&A task increased substantially through Retrieval-Augmented Generation (RAG) which brings in external knowledge. However, the main difficulty lies in balancing the inherent self-knowledge of LLMs with external information retrieval (IR). The current threshold-based methods apply one-dimensional static mechanisms with single criterion. As a result, their IR decisions might be irrelevant to the LLMs' response under difficult queries. To alleviate this problem, we propose Cognitive Convection of Self-Knowledge (CCSK). Different from traditional methods that maintain single fixed IR activation criteria, CCSK implements a dynamic joint decision process via a Siamese Network module and a Response Quality Model. The Siamese Network calculates the cosine similarity between the current query and the historical queries. The Response Quality Model evaluates the responses of LLMs through LightGBM. The final decision of the CCSK is derived from the outputs of the two modules, as well as text features fused using a multi-head attention mechanism. Extensive experiments on real-world datasets show that CCSK significantly enhances the model's effectiveness in information retrieval. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10498 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CCSK:Cognitive Convection of Self-Knowledge Based Retrieval Augmentation for Large Language Models Lu, Jianling Lv, Mingqi Chen, Tieming Information Retrieval Artificial Intelligence The performance of large language models (LLMs) in Q&A task increased substantially through Retrieval-Augmented Generation (RAG) which brings in external knowledge. However, the main difficulty lies in balancing the inherent self-knowledge of LLMs with external information retrieval (IR). The current threshold-based methods apply one-dimensional static mechanisms with single criterion. As a result, their IR decisions might be irrelevant to the LLMs' response under difficult queries. To alleviate this problem, we propose Cognitive Convection of Self-Knowledge (CCSK). Different from traditional methods that maintain single fixed IR activation criteria, CCSK implements a dynamic joint decision process via a Siamese Network module and a Response Quality Model. The Siamese Network calculates the cosine similarity between the current query and the historical queries. The Response Quality Model evaluates the responses of LLMs through LightGBM. The final decision of the CCSK is derived from the outputs of the two modules, as well as text features fused using a multi-head attention mechanism. Extensive experiments on real-world datasets show that CCSK significantly enhances the model's effectiveness in information retrieval. |
| title | CCSK:Cognitive Convection of Self-Knowledge Based Retrieval Augmentation for Large Language Models |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2504.10498 |