1 citations · 2 across the 12 of their papers we have counts for
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UR: Unify RAG and Reasoning through Reinforcement Learning
Weitao Li, Boran Xiang, Xiaolong Wang +5
Large Language Models (LLMs) have shown strong capabilities through two complementary paradigms: Retrieval-Augmented Generation (RAG) for knowledge grounding and Reinforcement Lear…
MUCAR: Benchmarking Multilingual Cross-Modal Ambiguity Resolution for Multimodal Large Language Models
Xiaolong Wang, Zhaolu Kang, Wangyuxuan Zhai +8
Multimodal Large Language Models (MLLMs) have demonstrated significant advances across numerous vision-language tasks. MLLMs have shown promising capability in aligning visual and…
Towards Transparent RAG: Fostering Evidence Traceability in LLM Generation via Reinforcement Learning
Jingyi Ren, Yekun Xu, Xiaolong Wang +4
Retrieval-Augmented Generation (RAG) delivers substantial value in knowledge-intensive applications. However, its generated responses often lack transparent reasoning paths that tr…
Perspective Transition of Large Language Models for Solving Subjective Tasks
Xiaolong Wang, Yuanchi Zhang, Ziyue Wang +5
Large language models (LLMs) have revolutionized the field of natural language processing, enabling remarkable progress in various tasks. Different from objective tasks such as com…