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Weiting Tan

Weiting Tan has 2 papers in our library, in 2024, most of them on Machine translation, Language models & pretraining, Multilingual & cross-lingual NLP; often with Haoran Xu, Lingfeng Shen, Yunmo Chen.

2 papers in our library · 2024

OpenAlex · h-index 4 · 21 works · 71 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 250 times in our library · ⭐ Paper Stars ranking: #4227

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

🎓Johns Hopkins UniversityBaltimore · United States2022–2026 · 19 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 4Research subjects and languages the work is about.Machine translation · 1 workBias, fairness & safety · 1 workLanguage models & pretraining · 1 workMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20242 papersWeiting Tan's papers of 2024

 Papers

The most cited first (OpenAlex and our library).

  1. Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation (2024 · 207 citations)
    Mid-sized LLMs (7B/13B) translate reasonably well but still trail GPT-4 and the strongest WMT systems. The authors trace part of this gap to supervised fine-tuning (SFT). SFT copies reference translations, so the model can only be as good as those references. It also never teaches the model to avoid output that is…
  2. The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts (2024 · 86 citations)
    This paper asks whether the safety behaviour of large language models holds up when the same harmful request is posed in languages with little training data.

 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 250 times (of the 141,840 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated

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