Title: Evidence-R1: Fine-Grained Evidence-Driven Explicit Reasoning and Implicit Reflection for Enhancing RAG Explainability via Reinforcement Learning
Primary area: interpretability and explainable AI
Keywords: RAG Explainability, Verifiable Citation

Abstract: Although Retrieval-Augmented Generation (RAG) has effectively mitigated the factual hallucination inherent in large language models (LLMs) by integrating external retrieved knowledge, LLMs still struggle with explainability and traceability. Existing research mainly focuses on generating responses with in-line citations, which can serve as evidence with factualness and verifiability. However, conducting fine-grained verification of such citations and mitigating citation errors remain significant challenges. 
To address this issue, we propose $\textbf{Evidence-R1}$, a novel RAG generator framework which drives explicit reasoning and implicit reflection based on sentence-level evidence. Specifically, explicit reasoning is defined as a reasoning process that strictly requires explicitly inferring answers from cited sentence-level evidence, while implicit reflection serves as an internal self-checking process that evaluates whether such answers are supported by the evidence through a special token, called $Sup$.
Nevertheless, this approach occasionally introduces asymmetry in the sentence-level evidence relied upon by the two processes. To tackle this, we introduce Multi-reward Dependence-aware Alignment ($\textbf{MRDAA}$), a multi-rule tree reward mechanism that enhances the consistency between the two processes through reinforcement learning.
Experimental results on the ALCE benchmark dataset demonstrate that Evidence-R1 outperforms existing state-of-the-art models in citation precision, even surpassing ChatGPT. Furthermore, by implementing fine-grained verification, Evidence-R1 has achieved significant improvements in interpretability and traceability.(https://anonymous.4open.science/r/Evidence-R1-1993699F/)