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Ed H. Chi

Ed H. Chi has 3 papers in our library, in 2022–2023, most of them on Language models & pretraining, Reasoning; often with Denny Zhou, Xinyun Chen, Barret Zoph.

3 papers in our library · 2022–2023

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📄 3 works in our library · 🔗 Cited 1,448 times in our library · ⭐ Paper Stars ranking: #1353

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

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 3 worksLanguage and Linguistics · 3 worksMathematics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3 worksReasoning · 2 works

💡 Concepts 2Key ideas and methods explained on our concept pages.Language model · 1 workLarge language models · 1 work

 Works by year

2020s

20232 papers20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Emergent Abilities of 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 (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 (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.

 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 →.

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