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Chenyu Zhu

Chenyu Zhu has 4 papers in our library, in 2025–2026, most of them on Multilingual & cross-lingual NLP, Reasoning; often with Tianqi Shi, Yefeng Liu, Bo Zeng.

4 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 2 · 8 works · 10 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 78 times in our library · ⭐ Paper Stars ranking: #8346

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 · China2025 · 1 papercurrent🎓Hangzhou Normal UniversityHangzhou · China2025 · 1 papercurrent

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 5The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArts and Humanities · 3 worksComputer Vision and Pattern Recognition · 4 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 2 worksMultilingual & cross-lingual NLP · 2 works

 Works by year

2020s

20262 papers20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Dynamic Early Exit in Reasoning Models (2025 · 74 citations)
    Large reasoning models such as DeepSeek-R1 and GPT-O1 often produce very long chains of thought (CoT). This "overthinking" adds latency and compute cost, and it can also turn a correct line of reasoning into a wrong one. In pilot runs, the authors forced models to answer at different points in their reasoning. Many…
  2. Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025 · 5 citations)
    Most benchmarks for LLM instruction following are English-only or plain machine translations, so they handle constraints that don't transfer across languages poorly.
  3. DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection (2026)
    Most benchmarks for detecting LLM-generated text (LGT) are English-only or cover languages unevenly. They also tend to ignore how people actually use LLMs, for example by asking a model to rework a draft. DetectRL-X addresses these gaps with 3.46 million samples in 8 languages from five language families and 6…
  4. I2E: From Image Pixels to Actionable Interactive Environments for Text-Guided Image Editing (2026)
    Most text-guided image editors repaint pixels end to end. This works for simple requests but fails on compositional instructions that need precise local control and reasoning about several objects. The authors trace the failures to three causes: planning and execution happen in one generation pass, there is no…

 Co-authors here

12 of 26 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 78 times (of the 149,439 papers whose bibliographies we read); 33 citing papers with a page here →.

Updated

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