# CCSK:Cognitive Convection of Self-Knowledge Based Retrieval Augmentation for Large Language Models

arXiv: https://arxiv.org/abs/2504.10498 · HTML: https://mlsanity.com/paper/2504.10498/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 15 of its 20 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 15 of 15)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022)
- [Active Retrieval Augmented Generation](https://mlsanity.com/paper/2305.06983/) (2023)
- [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018)
- [Retrieval-Augmented Generation for Natural Language Processing: A Survey](https://mlsanity.com/paper/2407.13193/) (2024)
- [Self-Knowledge Guided Retrieval Augmentation for Large Language Models](https://mlsanity.com/paper/2310.05002/) (2023)
- [Augmented Large Language Models with Parametric Knowledge Guiding](https://mlsanity.com/paper/2305.04757/) (2023)
- [Studying Large Language Model Behaviors Under Context-Memory Conflicts With Real Documents](https://mlsanity.com/paper/2404.16032/) (2024)
- [Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.18676/) (2024)
- [Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG](https://mlsanity.com/paper/2409.07691/) (2024)
- [Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness](https://mlsanity.com/paper/2205.01889/) (2022)
- [A Transformer-based Response Evaluator for Open-Domain Spoken Conversation](https://mlsanity.com/paper/2302.04424/) (2023)
- [Think-then-Act: A Dual-Angle Evaluated Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.13050/) (2024)
- [Knowledge Retrieval Based on Generative AI](https://mlsanity.com/paper/2501.04635/) (2025)

## Abstract (arXiv)

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.