🧑‍🔬 Longxu Dou

🏛️ Affiliations

🎓Harbin Institute of TechnologyHarbin · China2018–2025 · 19 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.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. RegMix: Data Mixture as Regression for Language Model Pre-training (2024 · 57 citations)
   The share of each data domain in a pre-training corpus strongly affects how well a large language model performs.
2. Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies (2024 · 46 citations)
   Standard scaling laws for LLMs predict loss from parameter count and training data but treat vocabulary size as fixed.
3. Sailor: Open Language Models for South-East Asia (2024 · 27 citations)
   Most large language models are trained mainly on English text and underperform on South-East Asian languages.
4. From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning (2023 · 24 citations)
   Instruction tuning helps language models handle tasks they never saw during training. Its training data, however, usually comes from human crowdsourcing, which is costly and limited in size, or from LLM generation, which is noisy because outputs may not match inputs. The authors propose adding symbolic tasks. These…
5. Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format (2026)
   Parallel test-time scaling (TTS) usually samples many answers using a single reasoning format chosen by a human.

All works

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

1. RegMix: Data Mixture as Regression for Language Model Pre-trainingarXiv:2407.01492Qian Liu, Xiaosen Zheng, Niklas Muennighoff, et al.2024 · 57 citations
2. Scaling Laws with Vocabulary: Larger Models Deserve Larger VocabulariesarXiv:2407.13623Chaofan Tao, Qian Liu, Longxu Dou, et al.2024 · 46 citations
3. Sailor: Open Language Models for South-East AsiaarXiv:2404.03608Longxu Dou, Qian Liu, Guangtao Zeng, et al.2024 · 27 citations
4. From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction TuningarXiv:2304.07995Qian Liu, Fan Zhou, Zhengbao Jiang, et al.2023 · 24 citations
5. Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable FormatFindings of the Association for Computational LinguisticsDingzirui Wang, Xuanliang Zhang, Rongyu Cao, et al.2026
6. How Can Synthetic Data Improve Multilingual Language Model Pretraining? A Data Quality Perspective ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetTongyao Zhu et al.2026

🤝 Co-authors here

12 of 23 co-authors with a page, the most papers together first.

• Min Lin ×4
• Qian Liu ×4
• Guangtao Zeng ×2
• Niklas Muennighoff ×2
• Binhua Li
• Chaofan Tao
• Dingzirui Wang
• Fan Zhou
• Fei Huang
• Jia Guo
• Jing Jiang
• Ngai Wong

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
• Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

🔗 Cited by

Papers of our library cite their works 141 times (of the 149,439 papers whose bibliographies we read); 35 citing papers with a page here →.

Updated 1 November 2026

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