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Yuan Sui

Yuan Sui has 3 papers in our library, in 2025–2026, most of them on Language models & pretraining, Question answering, Reasoning; often with Bryan Hooi, Yufei He, Yulin Chen.

3 papers in our library · 2025–2026

OpenAlex · ORCID · Wikidata · h-index 8 · 28 works · 307 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 127 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7561

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

🎓National University of SingaporeSingapore · Singapore2024–2025 · 8 paperscurrent🎓Shandong Normal UniversityJinan · China2021–2023 · 4 papers🎓Beijing University of TechnologyBeijing · China2021–2022 · 2 papers

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

 Fields, topics and concepts

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

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 1 workKnowledge graphs & ontologies · 1 workQuestion answering · 1 work

 Works by year

2020s

20261 paper20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Can Indirect Prompt Injection Attacks Be Detected and Removed? (2025 · 30 citations)
    Indirect prompt injection happens when an attacker plants malicious instructions in external content, such as web pages a tool retrieves, and the LLM then follows them instead of the user's request.
  2. Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question Answering (2025 · 16 citations)
    Most benchmarks for LLMs combined with knowledge graphs use closed-ended questions with short entity answers.
  3. Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models (2026 · 13 citations)
    Step-by-step reasoning in LLMs often wastes compute on dead-end solution paths. Early mistakes also tend to carry through long chains, because models seldom step back and change their overall approach. Meta-Reasoner addresses this with a separate high-level controller that runs alongside the base model during…

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study ↗arXiv:2305.13062 · no summary yetYuan Sui et al.2023 · 235 citations
  2. Can Indirect Prompt Injection Attacks Be Detected and Removed?Annual Meeting of the Association for Computational Linguistics (ACL)Yulin Chen, Haoran Li, Yuan Sui, et al.2025 · 30 citations
  3. Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question AnsweringAnnual Meeting of the Association for Computational Linguistics (ACL)Yuan Sui, Yufei He, Zifeng Ding, et al.2025 · 16 citations
  4. Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language ModelsFindings of the Association for Computational LinguisticsYuan Sui, Yufei He, Tri Cao, et al.2026 · 13 citations

 Co-authors here

7 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 127 times (of the 149,439 papers whose bibliographies we read); 27 citing papers with a page here →.

Updated

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