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Weitong Ruan

Weitong Ruan has 2 papers in our library, in 2026, most of them on Language models & pretraining, Evaluation & benchmarks, Semantic parsing & meaning representation; often with Aram Galstyan, Kai-Wei Chang, Rahul Gupta.

2 papers in our library · 2026

OpenAlex · h-index 5 · 19 works · 337 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 30 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #12273

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 States2020–2026 · 6 paperscurrent

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 7The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 workLanguage and Linguistics · 1 workSoftware · 1 work

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 1 workLanguage models & pretraining · 1 workSemantic parsing & meaning representation · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. AutoSUIT Bench - Automated Security UnIt Test Benchmark for LLM Coding (2026)
    Benchmarks that check whether LLM-written code is secure tend to rely on one of two approaches. Some use static analyzers such as CodeQL, which make many errors. Others use hand-written security tests, which limits them to a few dozen of the most common CWE categories. AutoSUIT addresses both problems with an…
  2. SWAN: Semantic Watermarking with Abstract Meaning Representation (2026)
    Watermarks for LLM-generated text are usually planted at the token level by biasing which tokens the decoder picks.

All works

6 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. SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · no summary yetLuoxin Chen et al.2020 · 137 citations
  2. Towards an ASR Error Robust Spoken Language Understanding System ↗Interspeech (USB) · no summary yetWeitong Ruan et al.2020 · 76 citations
  3. Enhance Robustness of Sequence Labelling with Masked Adversarial Training ↗Findings of the Association for Computational Linguistics: EMNLP 2020 · no summary yetLuoxin Chen et al.2020 · 69 citations
  4. AutoSUIT Bench - Automated Security UnIt Test Benchmark for LLM CodingFindings of the Association for Computational LinguisticsSamuel Osebe, Fan Yang, Junyi Li, et al.2026
  5. SWAN: Semantic Watermarking with Abstract Meaning RepresentationAnnual Meeting of the Association for Computational Linguistics (ACL)Ziping Ye, Gourab Dey, Christos Christodoulopoulos, et al.2026
  6. SkillHarm: Lifecycle-Aware Skill-Based Attacks via Automated Construction ↗arXiv (Cornell University) · no summary yetYuting Ning 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 30 times (of the 149,439 papers whose bibliographies we read); 3 citing papers with a page here →.

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