Elamur Research⌕
☰
home / authors / Chang Ma

Chang Ma

Chang Ma has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Reasoning; often with Junxian He, Cheng Hong Yang, Fangzhi Xu.

3 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 9 · 24 works · 823 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 75 times in our library · ⭐ Paper Stars ranking: #7965

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 · China2023–2025 · 3 paperscurrent🎓University of Hong KongPok Fu Lam · China2024–2025 · 2 paperscurrent🎓Harbin Institute of TechnologyHarbin · China2025 · 1 papercurrent🎓Beijing Forestry UniversityBeijing · China2016–2024 · 5 papers🎓Xidian UniversityXi'an · China2022–2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 11 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 3Research subjects and languages the work is about.Reasoning · 1 workLanguage models & pretraining · 1 workMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20261 paper20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents (2024 · 90 citations)
    Most benchmarks for LLM agents judge models only by whether they finish a task. This hides differences when nearly every model fails and reveals little about why. Many benchmarks also rely on single-turn or fully observable problems. AgentBoard (NeurIPS 2024, Datasets and Benchmarks track) addresses this with 1013…
  2. Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning (2025 · 8 citations)
    Genius tackles the problem of improving LLM reasoning after pre-training without any external supervision.
  3. How Can Synthetic Data Improve Multilingual Language Model Pretraining? A Data Quality Perspective (2026)
    Multilingual LLMs perform poorly in low-resource languages. The authors argue the main gap is not token volume but how little world knowledge the available text contains. They trained 1.1B TinyLlama models from scratch on noisy MADLAD-400 web data in six languages, all with the same token budget. Scores on simpler…

 Co-authors here

12 of 20 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 75 times (of the 149,439 papers whose bibliographies we read); 30 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/chang-ma/