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Lei Yu

Lei Yu has 5 papers in our library, in 2020–2026, most of them on Language models & pretraining, Machine translation; often with Jiafeng Guo, Xueqi Cheng, Yihang Wang.

5 papers in our library · 2020–2026

OpenAlex · h-index 0 · 5 works · 0 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 42 times in our library · ⭐ Paper Stars ranking: #7042

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

🏢Alibaba GroupHangzhou · China2026 · 1 papercurrent🏛️Chinese Academy of SciencesBeijing · China2026 · 1 papercurrent🔬Institute of Computing TechnologyBeijing · China2026 · 1 papercurrent🏢JingdongBeijing · China2026 · 1 papercurrent🎓University of Chinese Academy of SciencesBeijing · China2026 · 1 papercurrent

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 · 5 worksComputer Science · 5 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 5 worksComputer Vision and Pattern Recognition · 2 works

🧭 Topics 2Research subjects and languages the work is about.Machine translation · 1 workLanguage models & pretraining · 2 works

 Works by year

2020s

20263 papers20251 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Better Document-Level Machine Translation with Bayes' Rule (2020 · 72 citations)
    Translating whole documents requires cross-sentence context. For example, Chinese marks tense and number less explicitly than English, so the right English form often depends on other sentences. However, parallel documents for training are scarce. The authors address this by applying Bayes' rule, the classic noisy…
  2. Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations (2025 · 11 citations)
    Large language models often state wrong answers in a confident tone, which leads users to trust them too much.
  3. Sparse Brains are Also Adaptive Brains: Cognitive-Load-Aware Dynamic Activation for LLMs (2026 · 3 citations)
    Dense LLMs spend the same compute on every token, however easy or hard it is. Existing dynamic-activation methods each have a weakness. DejaVu does not generalize beyond ReLU models, Threshold Truncation (TT) costs too much overhead to give real speedups, and Griffin loses noticeable accuracy. The authors propose…
  4. Detoxification for LLM: From Dataset Itself (2026)
    Most LLM detoxification work acts late, through post-training or controlled decoding at inference time. That leaves the toxic knowledge absorbed from training data in place. This paper instead cleans the training corpus before any model learns from it. The authors propose HSPD (Hierarchical Semantic-Preserving…
  5. Distilling Large Embeddings via Hyperspherical Householder Quantization (2026)
    Generative retrieval trains a sequence model to output a document identifier for a query, so no dense vector index is needed.

 Co-authors here

12 of 19 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 42 times (of the 149,439 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated

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