42 citations · 70 across the 9 of their papers we have counts for
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cs.CV2025★ 1 cited
mKG-RAG: Leveraging Multimodal Knowledge Graphs in Retrieval-Augmented Generation for Knowledge-intensive VQA
Xu Yuan, Liangbo Ning, Qingqing Ye +2
Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for expanding the knowledge capacity of Multimodal Large Language Models (MLLMs) by incorporating external…
cs.AI2025
QA-Dragon: Query-Aware Dynamic RAG System for Knowledge-Intensive Visual Question Answering
Zhuohang Jiang, Pangjing Wu, Xu Yuan +2
Retrieval-Augmented Generation (RAG) has been introduced to mitigate hallucinations in Multimodal Large Language Models (MLLMs) by incorporating external knowledge into the generat…
cs.CL2025
HiBench: Benchmarking LLMs Capability on Hierarchical Structure Reasoning
Zhuohang Jiang, Pangjing Wu, Ziran Liang +7
Structure reasoning is a fundamental capability of large language models (LLMs), enabling them to reason about structured commonsense and answer multi-hop questions. However, exist…