Yihan Jiao

2025

HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation

Yihan Jiao, Zhehao Tan, Dan Yang, Duolin Sun, Jie Feng, Yue Shen +2

EMNLP 2025

Retrieval-augmented generation (RAG) has become a fundamental paradigm for addressing the challenges faced by large language models in handling real-time information and domain-specific problems. Traditional RAG systems primarily rely on the in-context learning (ICL) capabilities of the large langua

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PRGB Benchmark: A Robust Placeholder-Assisted Algorithm for Benchmarking Retrieval-Augmented Generation

HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation