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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.

4 papers in our library · 2025

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

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. 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.
  2. 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:
  3. 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.
  4. 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

6 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. 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
  2. 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

12 of 13 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 75 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

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