--- title: "Daiyi Peng — author profile and papers — Elamur Research" description: "Daiyi Peng: 2 papers on Elamur Research (2024), h-index 10 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/daiyi-peng/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/daiyi-peng/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Daiyi Peng # Daiyi Peng Daiyi Peng has 2 papers in our library, in 2024, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Jerry Wei](https://md.research.elamur.ai/free/en/authors/jerry-wei/), [Ruibo Liu](https://md.research.elamur.ai/free/en/authors/ruibo-liu/), [Andrew M. Dai](https://md.research.elamur.ai/free/en/authors/andrew-m-dai/). 2 papers in our library · 2024 [OpenAlex](https://openalex.org/A5049565925) · [**h-index 10**](https://openalex.org/authors/A5049565925) · [29 works](https://openalex.org/works?filter=author.id:A5049565925) · [1,625 citations](https://openalex.org/works?filter=author.id:A5049565925&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/daiyi-peng/#papers) · [🔗 Cited 2,500 times in our library](https://md.research.elamur.ai/free/en/authors/daiyi-peng/cited-by/) · [8 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #5063](https://md.research.elamur.ai/free/en/paper-stars/page/51/#star-5063) 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=Daiyi+Peng) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Daiyi+Peng) · [dblp](https://dblp.org/search?q=Daiyi+Peng) · [ACL Anthology](https://aclanthology.org/search/?q=Daiyi+Peng) ## Affiliations [🏢**Google** Mountain View · United States 2021–2022 · 4 papers](https://md.research.elamur.ai/free/en/companies/google/) · [🎓**Sichuan University** Chengdu · China 2005 · 3 papers](https://md.research.elamur.ai/free/en/universities/sichuan-university/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 7 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Safety Research · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) · [Social Sciences · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/) 🧭 Topics 1 Research subjects and languages the work is about.[Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) 💡 Concepts 1 Key ideas and methods explained on our concept pages.[Artificial intelligence · 1 work](https://md.research.elamur.ai/free/en/concepts/artificial-intelligence/) ## Works by year ### 2020s [**2024** 2 papers](https://md.research.elamur.ai/free/en/authors/daiyi-peng/year/2024/) ## Papers The most cited first (OpenAlex and our library). 1. **[Long-form factuality in large language models](https://md.research.elamur.ai/free/en/papers/wei-2024-long-form-factuality-large-language/)** *(2024 · 165 citations)* Large language models often make factual mistakes when they write answers several paragraphs long. Before this work there was no broad benchmark for this, and no cheap way to score such answers at scale. The Google DeepMind authors address the problem with three components. The first is LongFact, a set of 2,280… 2. **[Best Practices and Lessons Learned on Synthetic Data](https://md.research.elamur.ai/free/en/papers/liu-2024-best-practices-lessons-learned-synthetic/)** *(2024 · 119 citations)* Strong AI systems need large, varied and clean datasets, but such data is often scarce, expensive to label, or restricted by privacy rules. ## All works 10 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them. 1. **[DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection ↗](https://doi.org/10.1109/cvpr52688.2022.01667) IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yet**Yingwei Li et al.*2022 · 563 citations* 2. **[Long-form factuality in large language models](https://md.research.elamur.ai/free/en/papers/wei-2024-long-form-factuality-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jerry Wei, Chengrun Yang, Xinying Song, et al.*2024 · 165 citations* 3. [**Best Practices and Lessons Learned on Synthetic Data arXiv:2404.07503**Ruibo Liu, Jerry Wei, Fangyu Liu, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-best-practices-lessons-learned-synthetic/) 4. **[Domain Adaptive Transfer Learning with Specialist Models ↗](https://arxiv.org/abs/1811.07056) arXiv (Cornell University) · no summary yet**Jiquan Ngiam et al.*2018 · 87 citations* 5. **[AutoHAS: Efficient Hyperparameter and Architecture Search ↗](https://arxiv.org/abs/2006.03656) arXiv (Cornell University) · no summary yet**Xuanyi Dong et al.*2020 · 31 citations* 6. **[Evolving Reinforcement Learning Algorithms ↗](https://arxiv.org/abs/2101.03958) arXiv (Cornell University) · no summary yet**John D. Co-Reyes et al.*2021 · 22 citations* 7. **[PyGlove: Symbolic Programming for Automated Machine Learning ↗](https://arxiv.org/abs/2101.08809) arXiv (Cornell University) · no summary yet**Daiyi Peng et al.*2021 · 12 citations* 8. **[Brainformers: Trading Simplicity for Efficiency ↗](https://arxiv.org/abs/2306.00008) arXiv (Cornell University) · no summary yet**Yanqi Zhou et al.*2023 · 6 citations* 9. **[Domain Adaptive Transfer Learning ↗](https://openalex.org/W2903458748) no summary yet**Jiquan Ngiam et al.*2018 · 1 citation* 10. **[PyGlove: Efficiently Exchanging ML Ideas as Code ↗](https://arxiv.org/abs/2302.01918) arXiv (Cornell University) · no summary yet**Daiyi Peng et al.*2023* ## Co-authors here 12 of 16 co-authors with a page, the most papers together first. - [Jerry Wei](https://md.research.elamur.ai/free/en/authors/jerry-wei/) ×2 - [Ruibo Liu](https://md.research.elamur.ai/free/en/authors/ruibo-liu/) ×2 - [Andrew M. Dai](https://md.research.elamur.ai/free/en/authors/andrew-m-dai/) - [Chenglei Si](https://md.research.elamur.ai/free/en/authors/chenglei-si/) - [Chengrun Yang](https://md.research.elamur.ai/free/en/authors/chengrun-yang/) - [Da Huang](https://md.research.elamur.ai/free/en/authors/da-huang/) - [Denny Zhou](https://md.research.elamur.ai/free/en/authors/denny-zhou/) - [Diyi Yang](https://md.research.elamur.ai/free/en/authors/diyi-yang/) - [Dustin Tran](https://md.research.elamur.ai/free/en/authors/dustin-tran/) - [Fangyu Liu](https://md.research.elamur.ai/free/en/authors/fangyu-liu/) - [Jie Huang](https://md.research.elamur.ai/free/en/authors/jie-huang/) - [Quoc Viet Le](https://md.research.elamur.ai/free/en/authors/quoc-viet-le/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/): [NeurIPS 2024](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2024/) - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings ## Cited by Papers of our library cite their works 2,500 times (of the 149,439 papers whose bibliographies we read); [506 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/daiyi-peng/cited-by/). Updated 30 October 2026