---
title: "Hyung Won Chung — all works — Elamur Research"
description: "All 28 works of Hyung Won Chung in the Elamur Research library, the most cited first: our summaries where we have them, else the original."
canonical: "https://research.elamur.ai/free/en/authors/hyung-won-chung/works/"
---
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# Hyung Won Chung — all works

28 works · [← Hyung Won Chung](https://md.research.elamur.ai/free/en/authors/hyung-won-chung/)

1. [**PaLM: Scaling Language Modeling with Pathways arXiv:2204.02311**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 4,753 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
2. **[Large Language Models Encode Clinical Knowledge](https://md.research.elamur.ai/free/en/papers/singhal-2022-large-language-models-encode-clinical/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**Karan Singhal, Shekoofeh Azizi, Tao Tu, et al.*2022 · 4,492 citations*
3. **[Scaling Instruction-Finetuned Language Models ↗](https://arxiv.org/abs/2210.11416) arXiv:2210.11416 · no summary yet**Hyung Won Chung et al.*2022 · 3,392 citations*
4. **[BLOOM: A 176B-Parameter Open-Access Multilingual Language Model ↗](https://arxiv.org/abs/2211.05100) arXiv:2211.05100 · no summary yet**BigScience Workshop et al.*2022 · 1,606 citations*
5. **[Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://md.research.elamur.ai/free/en/papers/suzgun-2023-challenging-big-bench-tasks-whether/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mirac Suzgun, Nathan Scales, Nathanael Schärli, et al.*2023 · 1,119 citations*
6. [**The Flan Collection: Designing Data and Methods for Effective Instruction Tuning arXiv:2301.13688**Shayne Longpre, Le Hou, Tu Vu, et al.*2023 · 607 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-flan-collection-designing-data-methods/)
7. [**Language Models are Multilingual Chain-of-Thought Reasoners arXiv:2210.03057**Freda Shi, Mirac Suzgun, Markus Freitag, et al.*2022 · 494 citations*](https://md.research.elamur.ai/free/en/papers/shi-2022-language-models-multilingual-chain-thought/)
8. [**UL2: Unifying Language Learning Paradigms arXiv:2205.05131**Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al.*2022 · 347 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-ul2-unifying-language-learning-paradigms/)
9. [**Charformer: Fast Character Transformers via Gradient-based Subword Tokenization arXiv:2106.12672**Yi Tay, Vinh Q. Tran, Sebastian Ruder, et al.*2021 · 186 citations*](https://md.research.elamur.ai/free/en/papers/tay-2021-charformer-fast-character-transformers-gradient/)
10. [**Measuring short-form factuality in large language models arXiv:2411.04368**Jason Wei, Nguyen Karina, Hyung Won Chung, et al.*2024 · 160 citations*](https://md.research.elamur.ai/free/en/papers/wei-2024-measuring-short-form-factuality-large/)
11. [**Rethinking embedding coupling in pre-trained language models arXiv:2010.12821**Hyung Won Chung, Thibault Févry, Henry Tsai, et al.*2020 · 142 citations*](https://md.research.elamur.ai/free/en/papers/chung-2020-rethinking-embedding-coupling-pre-trained/)
12. [**Scaling Up Models and Data with t5x and seqio arXiv:2203.17189**Adam Roberts, Hyung Won Chung, Anselm Levskaya, et al.*2022 · 130 citations*](https://md.research.elamur.ai/free/en/papers/roberts-2022-scaling-up-models-data-texttt/)
13. **[Learning Compact Metrics for MT](https://md.research.elamur.ai/free/en/papers/pu-2021-learning-compact-metrics-mt/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Amy Pu, Hyung Won Chung, Ankur Parikh, et al.*2021 · 130 citations*
14. **[Do Transformer Modifications Transfer Across Implementations and Applications? ↗](https://aclanthology.org/2021.emnlp-main.465/) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · no summary yet**Sharan Narang et al.*2021 · 123 citations*
15. [**Deliberative Alignment: Reasoning Enables Safer Language Models arXiv:2412.16339**Melody Y. Guan, Manas Joglekar, Eric Wallace, et al.*2024 · 115 citations*](https://md.research.elamur.ai/free/en/papers/guan-2024-deliberative-alignment-reasoning-enables-safer/)
16. [**BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents arXiv:2504.12516**Jason Wei, Zhiqing Sun, Spencer Papay, et al.*2025 · 114 citations*](https://md.research.elamur.ai/free/en/papers/wei-2025-browsecomp-simple-yet-challenging-benchmark/)
17. **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers ↗](https://arxiv.org/abs/2109.10686) arXiv (Cornell University) · no summary yet**Yi Tay et al.*2021 · 104 citations*
18. **[Adversarial Attacks Against Medical Deep Learning Systems ↗](https://arxiv.org/abs/1804.05296) arXiv (Cornell University) · no summary yet**Samuel G. Finlayson et al.*2018 · 100 citations*
19. **[Improving Multilingual Models with Language-Clustered Vocabularies ↗](https://aclanthology.org/2020.emnlp-main.367/) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yet**Hyung Won Chung et al.*2020 · 92 citations*
20. **[Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?](https://md.research.elamur.ai/free/en/papers/tay-2023-scaling-laws-vs-model-architectures/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yi Tay, Mostafa Dehghani, Samira Abnar, et al.*2023 · 86 citations*
21. **[Neural Data Augmentation via Example Extrapolation ↗](https://arxiv.org/abs/2102.01335) arXiv (Cornell University) · no summary yet**Kenton Lee et al.*2021 · 79 citations*
22. [**Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models arXiv:2305.14705**Sheng Shen, Le Hou, Yanqi Zhou, et al.*2023 · 70 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-mixture-experts-meets-instruction-tuning/)
23. **[A Simple and Effective Positional Encoding for Transformers ↗](https://aclanthology.org/2021.emnlp-main.236/) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · no summary yet**Pu-Chin Chen et al.*2021 · 69 citations*
24. **[What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? ↗](https://arxiv.org/abs/2204.05832) arXiv:2204.05832 · no summary yet**Thomas Wang et al.*2022 · 62 citations*
25. **[Learning to Evaluate Translation Beyond English: BLEURT Submissions to the WMT Metrics 2020 Shared Task](https://md.research.elamur.ai/free/en/papers/sellam-2020-learning-evaluate-translation-beyond-english/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Thibault Sellam, Amy Pu, Hyung Won Chung, et al.*2020 · 55 citations*
26. **[UniMax: Fairer and more Effective Language Sampling for Large-Scale Multilingual Pretraining ↗](https://arxiv.org/abs/2304.09151) arXiv (Cornell University) · no summary yet**Hyung Won Chung et al.*2023 · 54 citations*
27. [**Transcending Scaling Laws with 0.1% Extra Compute arXiv:2210.11399**Yi Tay, Jason Wei, Hyung Won Chung, et al.*2022 · 42 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-transcending-scaling-laws-0-1/)
28. **[Finding Fast Transformers: One-Shot Neural Architecture Search by Component Composition ↗](https://arxiv.org/abs/2008.06808) arXiv (Cornell University) · no summary yet**Tsai, Henry et al.*2020 · 15 citations*