3 papers
cs.LG2026
Question-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation
Yuchen Yan, Peiyan Zhang, Zhihua Liu +4
Retrieval-augmented generation (RAG) has demonstrated its ability to enhance Large Language Models (LLMs) by integrating external knowledge sources. However, multi-hop questions, w…
cs.IR2025
RAG-IGBench: Innovative Evaluation for RAG-based Interleaved Generation in Open-domain Question Answering
Rongyang Zhang, Yuqing Huang, Chengqiang Lu +8
In real-world scenarios, providing user queries with visually enhanced responses can considerably benefit understanding and memory, underscoring the great value of interleaved imag…
cs.CL2025
SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment
Yuqing Huang, Rongyang Zhang, Qimeng Wang +9
Recent advancements in large language models (LLMs) have revolutionized natural language processing through their remarkable capabilities in understanding and executing diverse tas…