CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation
This addresses the issue of incomplete or misleading insights in knowledge-intensive tasks for users of large language models, representing a novel method for a known bottleneck rather than an incremental improvement.
The paper tackled the problem of existing RAG frameworks relying on semantic similarity, which limits their ability to distinguish true causal relationships, by introducing CDF-RAG, a framework that improves causal consistency and factual accuracy, demonstrating improved response accuracy and causal correctness over existing methods on four datasets.
Retrieval-Augmented Generation (RAG) has significantly enhanced large language models (LLMs) in knowledge-intensive tasks by incorporating external knowledge retrieval. However, existing RAG frameworks primarily rely on semantic similarity and correlation-driven retrieval, limiting their ability to distinguish true causal relationships from spurious associations. This results in responses that may be factually grounded but fail to establish cause-and-effect mechanisms, leading to incomplete or misleading insights. To address this issue, we introduce Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation (CDF-RAG), a framework designed to improve causal consistency, factual accuracy, and explainability in generative reasoning. CDF-RAG iteratively refines queries, retrieves structured causal graphs, and enables multi-hop causal reasoning across interconnected knowledge sources. Additionally, it validates responses against causal pathways, ensuring logically coherent and factually grounded outputs. We evaluate CDF-RAG on four diverse datasets, demonstrating its ability to improve response accuracy and causal correctness over existing RAG-based methods. Our code is publicly available at https://github.com/ elakhatibi/CDF-RAG.