TrustRAG: An Information Assistant with Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2502.13719v1
• Date: Wed, 19 Feb 2025 13:45:27 GMT
• Title: TrustRAG: An Information Assistant with Retrieval Augmented Generation
• Authors: Yixing Fan, Qiang Yan, Wenshan Wang, Jiafeng Guo, Ruqing Zhang, Xueqi Cheng,
• Abstract summary: TrustRAG is a novel framework that enhances acRAG from three perspectives: indexing, retrieval, and generation. We open-source the TrustRAG framework and provide a demonstration studio designed for excerpt-based question answering tasks.
• Score: 73.84864898280719
• License:
• Abstract: \Ac{RAG} has emerged as a crucial technique for enhancing large models with real-time and domain-specific knowledge. While numerous improvements and open-source tools have been proposed to refine the \ac{RAG} framework for accuracy, relatively little attention has been given to improving the trustworthiness of generated results. To address this gap, we introduce TrustRAG, a novel framework that enhances \ac{RAG} from three perspectives: indexing, retrieval, and generation. Specifically, in the indexing stage, we propose a semantic-enhanced chunking strategy that incorporates hierarchical indexing to supplement each chunk with contextual information, ensuring semantic completeness. In the retrieval stage, we introduce a utility-based filtering mechanism to identify high-quality information, supporting answer generation while reducing input length. In the generation stage, we propose fine-grained citation enhancement, which detects opinion-bearing sentences in responses and infers citation relationships at the sentence-level, thereby improving citation accuracy. We open-source the TrustRAG framework and provide a demonstration studio designed for excerpt-based question answering tasks \footnote{https://huggingface.co/spaces/golaxy/TrustRAG}. Based on these, we aim to help researchers: 1) systematically enhancing the trustworthiness of \ac{RAG} systems and (2) developing their own \ac{RAG} systems with more reliable outputs.

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