--- title: "Ed H. Chi — author profile and papers — Elamur Research" description: "Ed H. Chi: 3 papers on Elamur Research (2022–2023) — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/ed-h-chi/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/ed-h-chi/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Ed H. Chi # Ed H. Chi Ed H. Chi has 3 papers in our library, in 2022–2023, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/); often with [Denny Zhou](https://md.research.elamur.ai/free/en/authors/denny-zhou/), [Xinyun Chen](https://md.research.elamur.ai/free/en/authors/xinyun-chen/), [Barret Zoph](https://md.research.elamur.ai/free/en/authors/barret-zoph/). 3 papers in our library · 2022–2023 No OpenAlex profile matched yet. [📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/ed-h-chi/#papers) · [🔗 Cited 1,448 times in our library](https://md.research.elamur.ai/free/en/authors/ed-h-chi/cited-by/) · [⭐ Paper Stars ranking: #1353](https://md.research.elamur.ai/free/en/paper-stars/page/14/#star-1353) 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=Ed+H.+Chi) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Ed+H.+Chi) · [dblp](https://dblp.org/search?q=Ed+H.+Chi) · [ACL Anthology](https://aclanthology.org/search/?q=Ed+H.+Chi) ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 6 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Vision and Pattern Recognition · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Language and Linguistics · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Mathematics · 1 work](https://md.research.elamur.ai/free/en/shelves/mathematics/) 🧭 Topics 2 Research subjects and languages the work is about.[Language models & pretraining · 3 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning · 2 works](https://md.research.elamur.ai/free/en/topics/reasoning/) 💡 Concepts 2 Key ideas and methods explained on our concept pages.[Language model · 1 work](https://md.research.elamur.ai/free/en/concepts/language-model/) · [Large language models · 1 work](https://md.research.elamur.ai/free/en/concepts/large-language-models/) ## Works by year ### 2020s [**2023** 2 papers](https://md.research.elamur.ai/free/en/authors/ed-h-chi/year/2023/) · [**2022** 1 paper](https://md.research.elamur.ai/free/en/authors/ed-h-chi/year/2022/) ## Papers The most cited first (OpenAlex and our library). 1. **[Emergent Abilities of Large Language Models](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)** *(2022 · 2,269 citations)* Scaling laws let researchers predict how loss falls as models grow, yet some downstream skills do not follow such a smooth trend. 2. **[Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2023-take-step-back-evoking-reasoning/)** *(2023 · 126 citations)* Large language models still struggle with multi-step problems full of specific details. They lose sight of the governing rule or fail to recall the right facts, and Chain-of-Thought only partly helps. The Google DeepMind authors propose STEP-BACK PROMPTING, a two-stage few-shot technique. First, the model is shown… 3. **[Large Language Models as Analogical Reasoners](https://md.research.elamur.ai/free/en/papers/yasunaga-2023-large-language-models-analogical-reasoners/)** *(2023 · 74 citations)* Chain-of-thought prompting helps LLMs solve multi-step problems, but both common forms have drawbacks. The zero-shot variant gives only generic guidance, and the few-shot variant needs hand-written reasoning demonstrations for every task. Yasunaga et al. (Google DeepMind and Stanford) propose analogical prompting. It… ## Co-authors here 12 of 21 co-authors with a page, the most papers together first. - [Denny Zhou](https://md.research.elamur.ai/free/en/authors/denny-zhou/) ×3 - [Xinyun Chen](https://md.research.elamur.ai/free/en/authors/xinyun-chen/) ×2 - [Barret Zoph](https://md.research.elamur.ai/free/en/authors/barret-zoph/) - [Colin Raffel](https://md.research.elamur.ai/free/en/authors/colin-raffel/) - [Dani Yogatama](https://md.research.elamur.ai/free/en/authors/dani-yogatama/) - [Donald Metzler](https://md.research.elamur.ai/free/en/authors/donald-metzler/) - [Heng-Tze Cheng](https://md.research.elamur.ai/free/en/authors/heng-tze-cheng/) - [Huaixiu Steven Zheng](https://md.research.elamur.ai/free/en/authors/huaixiu-steven-zheng/) - [Jason Wei](https://md.research.elamur.ai/free/en/authors/jason-wei/) - [Jure Leskovec](https://md.research.elamur.ai/free/en/authors/jure-leskovec/) - [Maarten Bosma](https://md.research.elamur.ai/free/en/authors/maarten-bosma/) - [Michihiro Yasunaga](https://md.research.elamur.ai/free/en/authors/michihiro-yasunaga/) ## Cited by Papers of our library cite their works 1,448 times (of the 149,439 papers whose bibliographies we read); [341 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/ed-h-chi/cited-by/). Updated 2 November 2026