---
title: "Ngai Wong — author profile and papers — Elamur Research"
description: "Ngai Wong, University of Hong Kong: 6 papers on Elamur Research (2024–2026), h-index 28 — affiliations, topics, co-authors and who cites…"
canonical: "https://research.elamur.ai/free/en/authors/ngai-wong/"
---
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# Ngai Wong

Ngai Wong has 6 papers in our library, in 2024–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/); often with [Chaofan Tao](https://md.research.elamur.ai/free/en/authors/chaofan-tao/), [Runming Yang](https://md.research.elamur.ai/free/en/authors/runming-yang/), [Hayden Kwok-Hay So](https://md.research.elamur.ai/free/en/authors/hayden-kwok-hay-so/).

6 papers in our library · 2024–2026

[OpenAlex](https://openalex.org/A5072120057) · [ORCID](https://orcid.org/0000-0002-3026-0108) · [**h-index 28**](https://openalex.org/authors/A5072120057) · [352 works](https://openalex.org/works?filter=author.id:A5072120057) · [3,154 citations](https://openalex.org/works?filter=author.id:A5072120057&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 6 works in our library](https://md.research.elamur.ai/free/en/authors/ngai-wong/#papers) · [🔗 Cited 134 times in our library](https://md.research.elamur.ai/free/en/authors/ngai-wong/cited-by/) · [1 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #7363](https://md.research.elamur.ai/free/en/paper-stars/page/74/#star-7363)

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Ngai+Wong) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Ngai+Wong) · [dblp](https://dblp.org/search?q=Ngai+Wong) · [ACL Anthology](https://aclanthology.org/search/?q=Ngai+Wong)

## Affiliations

[🎓**University of Hong Kong** Pok Fu Lam · China 2000–2026 · 403 papersEmployment*current*](https://md.research.elamur.ai/free/en/universities/university-of-hong-kong/)

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 9 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [History and Philosophy of Science · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/history-and-philosophy-of-science/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · [Engineering · 1 work](https://md.research.elamur.ai/free/en/shelves/engineering/) · [Electrical and Electronic Engineering · 1 work](https://md.research.elamur.ai/free/en/shelves/engineering/electrical-and-electronic-engineering/)

🧭 Topics 3 Research subjects and languages the work is about.[Efficient models & compression · 2 works](https://md.research.elamur.ai/free/en/topics/efficient-models/) · [Language models & pretraining · 2 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/)

💡 Concepts 1 Key ideas and methods explained on our concept pages.[Scaling laws · 1 work](https://md.research.elamur.ai/free/en/concepts/scaling-laws/)

## Works by year

### 2020s

[**2026** 3 papers](https://md.research.elamur.ai/free/en/authors/ngai-wong/year/2026/) · [**2025** 2 papers](https://md.research.elamur.ai/free/en/authors/ngai-wong/year/2025/) · [**2024** 1 paper](https://md.research.elamur.ai/free/en/authors/ngai-wong/year/2024/)

## Papers

The most cited first (OpenAlex and our library).

1. **[Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2025-rethinking-kullback-leibler-divergence-knowledge/)** *(2025 · 47 citations)* Distilling large language models usually means training a student to match the teacher's next-token distribution with a Kullback-Leibler loss.
2. **[Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies](https://md.research.elamur.ai/free/en/papers/tao-2024-scaling-laws-vocabulary-larger-models/)** *(2024 · 46 citations)* Standard scaling laws for LLMs predict loss from parameter count and training data but treat vocabulary size as fixed.
3. **[TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer Review](https://md.research.elamur.ai/free/en/papers/chang-2025-treereview-dynamic-tree-questions-framework/)** *(2025 · 16 citations)* Peer review is under strain from growing submission volumes. LLM-based review assistants tend to miss details buried in long papers, give shallow critiques, or, as with multi-agent systems like MARG, consume very large numbers of tokens. TreeReview addresses this by treating reviewing as question answering organised…
4. **[Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation](https://md.research.elamur.ai/free/en/papers/zhang-2026-find-your-optimal-teacher-personalized/)** *(2026 · 13 citations)* Small LLMs are usually distilled by fine-tuning them on outputs from the most powerful available teacher. Prior work shows this can backfire: very strong teachers may produce answers that are too complex or too far from what the student can absorb. Earlier remedies (Mix, CAR) still make every candidate teacher answer…
5. **[Revisiting Model Interpolation for Efficient Reasoning](https://md.research.elamur.ai/free/en/papers/wu-2026-revisiting-model-interpolation-efficient-reasoning/)** *(2026 · 6 citations)* The paper looks at efficient reasoning: how to keep the accuracy of long chain-of-thought models while producing fewer tokens.
6. **[Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models](https://md.research.elamur.ai/free/en/papers/xiao-2026-exploring-layer-wise-information-effectiveness/)** *(2026 · 5 citations)* Compressing small language models (under 8B parameters) to about 2 bits per weight usually ruins their accuracy.

## All works

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

1. **[Scalable production of ultraflat and ultraflexible diamond membrane ↗](https://doi.org/10.1038/s41586-024-08218-x) Nature · no summary yet**Jixiang Jing et al.*2024 · 71 citations*
2. **[Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2025-rethinking-kullback-leibler-divergence-knowledge/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Taiqiang Wu, Chaofan Tao, Jiahao Wang, et al.*2025 · 47 citations*
3. [**Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies arXiv:2407.13623**Chaofan Tao, Qian Liu, Longxu Dou, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/tao-2024-scaling-laws-vocabulary-larger-models/)
4. **[TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer Review](https://md.research.elamur.ai/free/en/papers/chang-2025-treereview-dynamic-tree-questions-framework/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuan Chang, Ziyue Li, Hengyuan Zhang, et al.*2025 · 16 citations*
5. **[Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation](https://md.research.elamur.ai/free/en/papers/zhang-2026-find-your-optimal-teacher-personalized/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hengyuan Zhang, Shiping Yang, Xiao Liang, et al.*2026 · 13 citations*
6. **[Revisiting Model Interpolation for Efficient Reasoning](https://md.research.elamur.ai/free/en/papers/wu-2026-revisiting-model-interpolation-efficient-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Taiqiang Wu, Runming Yang, Tao Liu, et al.*2026 · 6 citations*
7. **[Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models](https://md.research.elamur.ai/free/en/papers/xiao-2026-exploring-layer-wise-information-effectiveness/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**He Xiao, Qingyao Yang, Dirui Xie, et al.*2026 · 5 citations*

## Co-authors here

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

- [Chaofan Tao](https://md.research.elamur.ai/free/en/authors/chaofan-tao/) ×3
- [Runming Yang](https://md.research.elamur.ai/free/en/authors/runming-yang/) ×3
- [Hayden Kwok-Hay So](https://md.research.elamur.ai/free/en/authors/hayden-kwok-hay-so/) ×2
- [Hengyuan Zhang](https://md.research.elamur.ai/free/en/authors/hengyuan-zhang/) ×2
- [Jiahao Wang](https://md.research.elamur.ai/free/en/authors/jiahao-wang/) ×2
- [Taiqiang Wu](https://md.research.elamur.ai/free/en/authors/taiqiang-wu/) ×2
- [Angel X. Chang](https://md.research.elamur.ai/free/en/authors/angel-x-chang/)
- [Chenming Shang](https://md.research.elamur.ai/free/en/authors/chenming-shang/)
- [Jing Xiong](https://md.research.elamur.ai/free/en/authors/jing-xiong/)
- [Longxu Dou](https://md.research.elamur.ai/free/en/authors/longxu-dou/)
- [Min Lin](https://md.research.elamur.ai/free/en/authors/min-lin/)
- [Niklas Muennighoff](https://md.research.elamur.ai/free/en/authors/niklas-muennighoff/)

## Venues

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

- [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×2: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) ×2
- [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/) ×2: [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)
- [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/): [COLING 2025](https://md.research.elamur.ai/free/en/venues/coling/2025/)
- [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/): [EMNLP 2025](https://md.research.elamur.ai/free/en/venues/emnlp/2025/)
- Nature

## Cited by

Papers of our library cite their works 134 times (of the 149,439 papers whose bibliographies we read); [40 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/ngai-wong/cited-by/).

Updated 2 November 2026