Shenglai Zeng
Shenglai Zeng has 4 papers in our library, in 2025, most of them on Language models & pretraining, Information retrieval & search; often with Jiliang Tang, Yue Xing, Hanqing Lu.
OpenAlex · ORCID · h-index 9 · 39 works · 338 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 75 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #7440
Affiliations
Fields, topics and concepts
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArtificial Intelligence · 4 worksArts and Humanities · 3 worksLanguage and Linguistics · 3 worksInformation Systems · 2 worksComputer Vision and Pattern Recognition · 2 works
🧭 Topics 2Research subjects and languages the work is about.Information retrieval & search · 1 workLanguage models & pretraining · 1 work
Works by year
2020s
Papers
- Unveiling Privacy Risks in LLM Agent Memory (2025 · 33 citations)
LLM agents often keep a long-term memory of earlier user queries paired with the solutions they produced, and they reuse these records as in-context examples. - Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective (2025 · 14 citations)
RAG systems often fail when the retrieved passages are misleading (for example, after PoisonedRAG-style injection of false answers) or only loosely related to the question. LLMs tend to trust such context over what they already know. The authors frame knowledge checking as four tasks: - Empowering GraphRAG with Knowledge Filtering and Integration (2025 · 12 citations)
Graph retrieval-augmented generation (GraphRAG) supplements LLMs with facts from knowledge graphs, but the authors show it has two weaknesses. - Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach (2025 · 5 citations)
LLMs that receive retrieved passages, as in RAG, tend to trust that text too much. Misleading or off-topic passages can override knowledge the model already has. The authors define a "context-robust" LLM by four behaviours:
All works
- The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) ↗Findings of the Association for Computational Linguistics: ACL 2024 · no summary yetShenglai Zeng et al.2024 · 146 citations
- Unveiling Privacy Risks in LLM Agent MemoryAnnual Meeting of the Association for Computational Linguistics (ACL)Bo Wang, Weiyi He, Shenglai Zeng, et al.2025 · 33 citations
- Towards Knowledge Checking in Retrieval-augmented Generation: A Representation PerspectiveConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Shenglai Zeng, Jiankun Zhang, Bingheng Li, et al.2025 · 14 citations
- Empowering GraphRAG with Knowledge Filtering and IntegrationConference on Empirical Methods in Natural Language Processing (EMNLP)Kai Guo, Harry Shomer, Shenglai Zeng, et al.2025 · 12 citations
- Towards Context-Robust LLMs: A Gated Representation Fine-tuning ApproachAnnual Meeting of the Association for Computational Linguistics (ACL)Shenglai Zeng, Pengfei He, Kai Guo, et al.2025 · 5 citations
- Attn-GS: Attention-Guided Context Compression for Efficient Personalized LLMs ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetShenglai Zeng et al.2026
Co-authors here
- Jiliang Tang ×3
- Yue Xing ×3
- Hanqing Lu ×2
- Hui Liu ×2
- Kai Guo ×2
- Pengfei He ×2
- Tianqi Zheng ×2
- Bo Wang
- Jiankun Zhang
- Monica Xiao Cheng
- Yu Wang
- Yuping Lin
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×3: ACL 2025 ×2
- Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2025
- Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): NAACL 2025
- Findings of the Association for Computational Linguistics: ACL 2024
Cited by
Updated
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