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

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 this https URL.

12 pages, 1 figure, 7 tables. The 1st International Workshop on Retrieval-Driven Generative AI & ScienceON AI Challenge 2025@CIKM

Artificial Intelligence (cs.AI); Digital Libraries (cs.DL); Information Retrieval (cs.IR)

arXiv:2607.22706 [cs.AI]

(or arXiv:2607.22706v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.22706 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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