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Tianqi Zheng

Tianqi Zheng has 2 papers in our library, in 2025, most of them on Language models & pretraining, Information retrieval & search; often with Hanqing Lu, Hui Liu, Shenglai Zeng.

2 papers in our library · 2025

OpenAlex · ORCID · h-index 2 · 14 works · 12 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 15 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #9159

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

🏢AmazonSeattle · United States2024–2026 · 4 paperscurrent🎓Guangxi UniversityNanning · China2026 · 1 paperEmploymentcurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksInformation Systems · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Information retrieval & search · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. 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:
  2. 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

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. 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
  2. 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
  3. AmazonQAC: A Large-Scale, Naturalistic Query Autocomplete Dataset ↗Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track · no summary yetDante Everaert et al.2024 · 3 citations
  4. 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

10 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 15 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

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