Improving Context Fidelity via Native Retrieval-Augmented Reasoning
Authors: name: Suyuchen Wang, name: Jinlin Wang, name: Xinyu Wang, name: Shiqi Li, name: Xiangru Tang, name: Sirui Hong, name: Xiao-Wen Chang, name: Chenglin Wu, name: Bang Liu

Large language models (LLMs) often struggle with context fidelity, producing
inconsistent answers when responding to questions based on provided
information. Existing approaches either rely on expensive supervised
fine-tuning to generate evidence post-answer or train models to perform web
searches without necessarily improving utilization of the given context. We
propose CARE, a novel native retrieval-augmented reasoning framework that
teaches LLMs to explicitly integrate in-context evidence within their reasoning
process with the model's own retrieval capabilities. Our method requires
limited labeled evidence data while significantly enhancing both retrieval
accuracy and answer generation performance through strategically retrieved
in-context tokens in the reasoning chain. Extensive experiments on multiple
real-world and counterfactual QA benchmarks demonstrate that our approach
substantially outperforms supervised fine-tuning, traditional
retrieval-augmented generation methods, and external retrieval solutions. This
work represents a fundamental advancement in making LLMs more accurate,
reliable, and efficient for knowledge-intensive tasks.