VeriCite: Towards Reliable Citations in Retrieval-Augmented Generation via Rigorous Verification

2025-10-13

AI Summary

Large language models (LLMs) in retrieval-augmented generation (RAG) frequently exhibit hallucination, and existing citation generation methods either require extensive annotated data for fine-tuning or struggle to jointly manage multiple evidence sources, resulting in suboptimal performance. Method: We propose VeriCite, the first framework to integrate natural language inference (NLI) models directly into the generation pipeline, establishing a closed-loop “retrieve–verify–generate” paradigm: (1) retrieve candidate evidence; (2) dynamically assess claim veracity via NLI to filter high-confidence evidence; and (3) jointly optimize answer generation and citation selection—without any model fine-tuning. VeriCite incorporates context decomposition, evidence alignment, and utility-aware evaluation. Results: Evaluated across five open-source LLMs and four benchmark datasets, VeriCite significantly improves citation accuracy while preserving answer correctness, consistently outperforming state-of-the-art fine-tuning and post-hoc citation methods.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Retrieval-Augmented Generation (RAG) has emerged as a crucial approach for enhancing the responses of large language models (LLMs) with external knowledge sources. Despite the impressive performance in complex question-answering tasks, RAG still struggles with hallucinations. Attributing RAG-generated content through in-line citations has demonstrated potential in reducing hallucinations and facilitating human verification. Existing citation generation methods primarily rely on either fine-tuning the generator or employing post-processing approaches for citation matching. However, the former approach demands substantial annotated data and computational resources, while the latter often encounters difficulties in managing multiple citations and frequently produces suboptimal results. In this paper, we introduce a novel framework, called VeriCite, designed to rigorously validate supporting evidence and enhance answer attribution. Specifically, VeriCite breaks down into a three-stage generation: 1) The initial answer generation first generates a response based on all available contexts and has its claims verified through the NLI model; 2) the supporting evidence selection assesses the utility of each document and extracts useful supporting evidences; 3) the final answer refinement integrates the initial response and collected evidences to produce the final, refined answer.We conduct experiments across five open-source LLMs and four datasets, demonstrating that VeriCite can significantly improve citation quality while maintaining the correctness of the answers.

Problem

• Reducing hallucinations in retrieval-augmented generation systems
• Improving citation reliability for generated content verification
• Addressing limitations of existing citation generation methods

Innovation

• Three-stage generation process for citation verification
• Uses NLI model to verify claims in initial answers
• Integrates supporting evidence for final answer refinement