Jie Jiang
Jie Jiang has 3 papers in our library, in 2024–2026, most of them on Information retrieval & search; often with Huan Yu, Liqun Liu, Peng Shu.
OpenAlex · h-index 1 · 8 works · 1 citations OpenAlex, 2026-10-14
📄 3 works in our library · 🔗 Cited 95 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #4933
Affiliations
Fields, topics and concepts
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksInformation Systems · 2 worksComputer Vision and Pattern Recognition · 1 work
🧭 Topics 1Research subjects and languages the work is about.Information retrieval & search · 1 work
💡 Concepts 1Key ideas and methods explained on our concept pages.Retrieval-augmented generation · 1 work
Works by year
2020s
Papers
- Retrieval-Augmented Generation for AI-Generated Content: A Survey (2024 · 301 citations)
Generative models for text, code, images, video and other media still have trouble with outdated knowledge, rare (long-tail) facts, leakage of private training data, and costly training and inference. - Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training (2026 · 2 citations)
Agentic RAG systems trained with reinforcement learning, such as Search-R1, usually receive a single binary reward for the final answer. - SSR-A: Spatial- and Semantic-Aware Instructions and Curriculum Reinforcement for Advertisement Compliant Rectification (2026)
Ad platforms reject millions of non-compliant image ads every day, and fixing them by hand does not scale.
All works
- Retrieval-Augmented Generation for AI-Generated Content: A SurveyarXiv:2402.19473Penghao Zhao, Hailin Zhang, Qinhan Yu, et al.2024 · 301 citations
- Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG TrainingAnnual Meeting of the Association for Computational Linguistics (ACL)Tianle Xia, Ming Xu, Lingxiang Hu, et al.2026 · 2 citations
- Internalizing Multi-Agent Reasoning for Accurate and Efficient LLM-based Recommendation ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetYang Wu et al.2026
- AT\texttwosuperiorPO: Agentic Turn-based Policy Optimization via Tree Search ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetZefang Zong et al.2026
Co-authors here
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×3: ACL 2026 ×2
- Findings of the Association for Computational Linguistics: ACL 2026
Cited by
Updated
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