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Lingfeng Shen

Lingfeng Shen 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, Weiting Tan, Yunmo Chen.

2 papers in our library · 2024

OpenAlex · ORCID · h-index 8 · 34 works · 209 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 325 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #4225

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

🎓Zhejiang UniversityHangzhou · China2022–2026 · 7 paperscurrent🔬State Key Laboratory Fluid Power and Mechatronic SystemsHangzhou · China2022–2026 · 4 paperscurrent🎓Johns Hopkins UniversityBaltimore · United States2022–2024 · 11 papers🏢TencentShenzhen · China2022–2023 · 4 papers🎓Henan UniversityKaifeng · China2024–2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions 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 papersLingfeng Shen'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.

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. Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine TranslationarXiv:2401.08417Haoran Xu, Amr Sharaf, Yunmo Chen, et al.2024 · 207 citations
  2. The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual ContextsFindings of the Association for Computational LinguisticsLingfeng Shen, Weiting Tan, Sihao Chen, et al.2024 · 86 citations
  3. SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text GenerationProceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · original ↗ · no summary yetAbe Hou et al.2024 · 74 citations
  4. On the Evaluation Metrics for Paraphrase GenerationProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · original ↗ · no summary yetLingfeng Shen et al.2022 · 65 citations

 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 325 times (of the 141,840 papers whose bibliographies we read); 13 citing papers with a page here →.

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