TrustRAG: An Information Assistant with Retrieval Augmented Generation

Code

• github.com/gomate-community/trustrag
  OfficialIn papernone★ 1,238

Abstract

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 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 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 https://huggingface.co/spaces/golaxy/TrustRAG. Based on these, we aim to help researchers: 1) systematically enhancing the trustworthiness of RAG systems and (2) developing their own RAG systems with more reliable outputs.

Tasks

Reproductions