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Mahani Aljunied

Mahani Aljunied has 4 papers in our library, in 2023–2025, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Low-resource languages; often with Lidong Bing, Chaoqun Liu, Hou Pong Chan.

4 papers in our library · 2023–2025

OpenAlex · h-index 7 · 18 works · 149 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 251 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5903

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 · China2022–2026 · 7 paperscurrent🏢Alibaba DAMO AcademyHangzhou · China2022–2024 · 4 papers🏢Alibaba GroupGeorge Town · Cayman Islands2022–2024 · 4 papers

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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

🧭 Topics 7Research subjects and languages the work is about.Language models & pretraining · 3 worksEnglish · 1 workLow-resource languages · 1 workEvaluation & benchmarks · 1 workInformation retrieval & search · 1 workVision & language · 1 workMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20252 papers20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. SeaLLMs — Large Language Models for Southeast Asia (2023 · 92 citations)
    Mainstream LLMs are trained mostly on English. As a result, they handle Southeast Asian languages poorly, especially those written in non-Latin scripts, which standard BPE tokenizers break into very long token sequences. SeaLLMs are open models for Southeast Asian languages, built by adapting English-centric…
  2. Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts (2024 · 55 citations)
    Large language models can learn tasks from a few in-context examples, but this ability is strongest in English and a few other well-resourced languages.
  3. M-LongDoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework (2025 · 21 citations)
    Question answering over real-world documents is difficult for large multimodal models. Such documents can run to hundreds of pages and mix text, figures and tables. Earlier benchmarks mostly used documents under 50 pages and short extractive answers.
  4. Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers (2025 · 8 citations)
    Open multilingual LLMs tend to favor languages with abundant web text, such as French or German. Populous but data-poor languages like Hindi, Bengali or Urdu remain underserved. Babel, from Alibaba's DAMO Academy, targets the 25 languages with the most speakers, which together cover roughly 7 billion people.

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models ↗arXiv:2306.05179 · no summary yetWenxuan Zhang et al.2023 · 150 citations
  2. SeaLLMs — Large Language Models for Southeast AsiaarXiv:2312.00738Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, et al.2023 · 92 citations
  3. Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse PromptsAnnual Meeting of the Association for Computational Linguistics (ACL)Xuan-Phi Nguyen, Mahani Aljunied, Shafiq Joty, et al.2024 · 55 citations
  4. M-LongDoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning FrameworkConference on Empirical Methods in Natural Language Processing (EMNLP)Yew Ken Chia, Liying Cheng, Hou Pong Chan, et al.2025 · 21 citations
  5. Babel: Open Multilingual Large Language Models Serving Over 90% of Global SpeakersarXiv:2503.00865Yiran Zhao, Chaoqun Liu, Yue Deng, et al.2025 · 8 citations

 Co-authors here

12 of 21 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 251 times (of the 149,439 papers whose bibliographies we read); 79 citing papers with a page here →.

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