After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in RAG
This addresses trustworthiness issues in real-world RAG applications for LLM users, though it appears incremental as it builds on existing RAG approaches.
The paper tackles the problem of balancing conflicting internal and external knowledge in retrieval-augmented generation (RAG) systems by proposing the BRIDGE framework, which dynamically selects response strategies and improves accuracy by 5-15% over baselines.
Retrieval-augmented generation (RAG) systems face critical challenges in balancing internal (parametric) and external (retrieved) knowledge, especially when these sources conflict or are unreliable. To analyze these scenarios comprehensively, we construct the Trustworthiness Response Dataset (TRD) with 36,266 questions spanning four RAG settings. We reveal that existing approaches address isolated scenarios-prioritizing one knowledge source, naively merging both, or refusing answers-but lack a unified framework to handle different real-world conditions simultaneously. Therefore, we propose the BRIDGE framework, which dynamically determines a comprehensive response strategy of large language models (LLMs). BRIDGE leverages an adaptive weighting mechanism named soft bias to guide knowledge collection, followed by a Maximum Soft-bias Decision Tree to evaluate knowledge and select optimal response strategies (trust internal/external knowledge, or refuse). Experiments show BRIDGE outperforms baselines by 5-15% in accuracy while maintaining balanced performance across all scenarios. Our work provides an effective solution for LLMs' trustworthy responses in real-world RAG applications.