Utility-Focused LLM Annotation for Retrieval and Retrieval-Augmented Generation. - Hengran Zhang; Minghao Tang 001; Keping Bi; Jiafeng Guo; Shihao Liu; Daiting Shi; Dawei Yin 001; Xueqi Cheng (EMNLP, 2025)

Injecting External Knowledge into the Reasoning Process Enhances Retrieval-Augmented Generation. - Minghao Tang 001; Shiyu Ni; Jiafeng Guo; Keping Bi (SIGIR-AP, 2025)

Leveraging LLMs for Utility-Focused Annotation: Reducing Manual Effort for Retrieval and RAG. - Hengran Zhang; Minghao Tang 001; Keping Bi; Jiafeng Guo; Shihao Liu; Daiting Shi; Dawei Yin 001; Xueqi Cheng (CoRR, 2025)

The Role of Parametric Injection-A Systematic Study of Parametric Retrieval-Augmented Generation. - Minghao Tang 001; Shiyu Ni; Jingtong Wu; Zengxin Han; Keping Bi (CoRR, 2025)

Annotation-Efficient Universal Honesty Alignment. - Shiyu Ni; Keping Bi; Jiafeng Guo; Minghao Tang 001; Jingtong Wu; Zengxin Han; Xueqi Cheng (CoRR, 2025)

Training Dense Retrievers with Multiple Positive Passages. - Benben Wang; Minghao Tang 001; Hengran Zhang; Jiafeng Guo; Keping Bi (CoRR, 2026)

Beyond Relevance: Utility-Centric Retrieval in the LLM Era. - Hengran Zhang; Minghao Tang 001; Keping Bi; Jiafeng Guo (CoRR, 2026)