3 papers
cs.CL2025
R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning
Qingfei Zhao, Ruobing Wang, Dingling Xu +2
Large language models (LLMs) have notably progressed in multi-step and long-chain reasoning. However, extending their reasoning capabilities to encompass deep interactions with sea…
cs.IR2025
UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation
Yuxuan Chen, Dewen Guo, Sen Mei +12
Retrieval-Augmented Generation (RAG) significantly enhances the performance of large language models (LLMs) in downstream tasks by integrating external knowledge. To facilitate res…
cs.CL2024
PrefRAG: Preference-Driven Multi-Source Retrieval Augmented Generation
Qingfei Zhao, Ruobing Wang, Yukuo Cen +3
Retrieval-Augmented Generation (RAG) has emerged as a reliable external knowledge augmentation technique to mitigate hallucination issues and parameterized knowledge limitations in…