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Li Shen

Li Shen has 4 papers in our library, in 2023–2026, most of them on Machine translation, Language models & pretraining, Bias, fairness & safety; often with Liang Ding, Dacheng Tao, Baolei Zhang.

4 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 3 · 23 works · 43 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 123 times in our library · ⭐ Paper Stars ranking: #5650

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

🎓Nankai UniversityTianjin · China2026 · 1 papercurrent🎓Sun Yat-sen UniversityGuangzhou · China2026 · 1 papercurrent🎓University of Science and Technology of ChinaHefei · China2002–2020 · 9 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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).Artificial Intelligence · 4 worksComputer Science · 4 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksDecision Sciences · 1 workManagement Science and Operations Research · 1 work

🧭 Topics 3Research subjects and languages the work is about.Machine translation · 1 workBias, fairness & safety · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20262 papers20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Towards Making the Most of ChatGPT for Machine Translation (2023 · 190 citations)
    Earlier evaluations found that ChatGPT matches commercial engines on high-resource language pairs but falls behind on low-resource and distant pairs.
  2. MerA: Merging Pretrained Adapters For Few-Shot Learning (2023 · 11 citations)
    Adapter tuning trains only small bottleneck modules inside a frozen pretrained language model. These modules start from random weights, so they tend to underperform when only a handful of labeled examples are available. AdapterFusion reuses previously trained adapters, but its attention-style composition layers add…
  3. CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning (2026 · 3 citations)
    Providers offering fine-tuning as a service face a known risk: a user can upload harmful data and erase a model's safety alignment.
  4. Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling (2026)
    Zeroth-Order (ZO) fine-tuning of LLMs needs only forward passes and therefore uses about as much memory as inference.

 Co-authors here

12 of 15 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 123 times (of the 149,439 papers whose bibliographies we read); 26 citing papers with a page here →.

Updated

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