MPR-CiteG: Enhancing RAG with Multi-Portfolio Retrieval and Citation-Grounded Generation

2026-07-20

AI Summary

This work addresses the challenges of inefficient retrieval and source-unverified generation in generative AI, which often lead to hallucinations. To mitigate these issues, the authors propose the MPR-CiteG framework, which integrates a Multi-Path Retriever (MPR) to efficiently gather diverse, relevant information and a Citation-Anchored Generation module (CiteG) that ensures every generated statement is grounded in accurate, traceable sources. This framework represents the first synergistic integration of multi-path retrieval with citation-anchored generation. Evaluated on the ScienceON AI Challenge dataset, MPR-CiteG significantly reduces hallucination rates while enhancing answer accuracy and credibility, securing second place in the competition and demonstrating marked improvements in factual consistency and reliability of large language model outputs.

Abstract

This paper presents the MPR-CiteG framework, which achieved second place in the ScienceON AI Challenge by addressing two fundamental challenges in generative AI: inefficient retrieval and the absence of source verification. We propose a dual-component system, termed MPR-CiteG, in which the Multi-Portfolio Retriever (MPR) efficiently retrieves diverse and relevant information, while the Citation-Grounded Generation (CiteG) module ensures that every generated output remains factually consistent and explicitly attributed to its source. MPR-CiteG represents a significant step toward building more trustworthy and accurate LLMs that are not only capable of generating information but also of grounding their responses in reliable evidence, thereby mitigating common issues like model hallucination. Extensive experiments on the challenge dataset validate the effectiveness and reliability of our approach. Our code is available at https://github.com/2noweyh/MPR-citeG.

Problem

• retrieval inefficiency
• source verification
• model hallucination
• factually consistent generation
• reliable evidence grounding

Innovation

• Multi-Portfolio Retrieval
• Citation-Grounded Generation
• Retrieval-Augmented Generation
• Fact Consistency
• Source Attribution