--- title: "Shixiang Shane Gu — author profile and papers" description: "Shixiang Shane Gu: 2 papers on Elamur Research (2022), h-index 3 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/shixiang-shane-gu/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/shixiang-shane-gu/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Shixiang Shane Gu # Shixiang Shane Gu Shixiang Shane Gu works on large language models, with a focus on how such models can be made to reason through multi-step problems and to handle tasks they were not trained on directly. The papers here, both from 2022, sit within [language models and pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) and [reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/). They share a practical question: how much can be gained from the way a model is prompted or finetuned, rather than from new architectures? The usual method is empirical, testing one approach across many benchmarks and model sizes. The most cited of these papers, [Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/), shows that a model can produce step-by-step reasoning without any hand-crafted worked examples. A single generic trigger phrase placed after the question prompts the model to reason aloud, and a second prompt then extracts the final answer. The same template, left unchanged, was evaluated on arithmetic word problems, symbolic puzzles, commonsense question answering and logic tasks. The second paper, [Scaling Instruction-Finetuned Language Models](https://md.research.elamur.ai/free/en/papers/chung-2022-scaling-instruction-finetuned-language-models/), studies finetuning on a very large collection of tasks phrased as instructions. It asks how results change as the number of tasks grows, as models get larger, and when training includes examples with written-out reasoning. It applies this procedure to T5, PaLM and U-PaLM models across a wide range of sizes. Together the two papers trace a single line of work on chain-of-thought reasoning, approached once through prompting alone and once through instruction finetuning. Co-authors on the finetuning study include [Hyung Won Chung](https://md.research.elamur.ai/free/en/authors/hyung-won-chung/), [Yi Tay](https://md.research.elamur.ai/free/en/authors/yi-tay/), [Shayne Longpre](https://md.research.elamur.ai/free/en/authors/shayne-longpre/) and [Mostafa Dehghani](https://md.research.elamur.ai/free/en/authors/mostafa-dehghani/). The zero-shot reasoning paper was written with a different group of collaborators. Shixiang Shane Gu has 2 papers in our library, in 2022, 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 [Albert Webson](https://md.research.elamur.ai/free/en/authors/albert-webson/), [Barret Zoph](https://md.research.elamur.ai/free/en/authors/barret-zoph/), [Hyung Won Chung](https://md.research.elamur.ai/free/en/authors/hyung-won-chung/). 2 papers in our library · 2022 [OpenAlex](https://openalex.org/A5127777713) · [**h-index 3**](https://openalex.org/authors/A5127777713) · [4 works](https://openalex.org/works?filter=author.id:A5127777713) · [1,570 citations](https://openalex.org/works?filter=author.id:A5127777713&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/shixiang-shane-gu/#papers) · [🔗 4,399 citations in our library](https://md.research.elamur.ai/free/en/authors/shixiang-shane-gu/cited-by/) · [⭐ Paper Star rank #432](https://md.research.elamur.ai/free/en/paper-stars/page/5/#star-432) 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=Shixiang+Shane+Gu) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Shixiang+Shane+Gu) · [dblp](https://dblp.org/search?q=Shixiang+Shane+Gu) · [ACL Anthology](https://aclanthology.org/search/?q=Shixiang+Shane+Gu) ## Topics [Language models & pretraining (2)](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning (1)](https://md.research.elamur.ai/free/en/topics/reasoning/) ## Works by year ### 2020s [**2022** 2 papers](https://md.research.elamur.ai/free/en/authors/shixiang-shane-gu/year/2022/) ## Papers The most cited first (OpenAlex and our library). 1. **[Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/)** *(2022 · 3,780 citations)* The paper asks whether large language models need hand-written, task-specific worked examples to solve multi-step reasoning problems. 2. **[Scaling Instruction-Finetuned Language Models](https://md.research.elamur.ai/free/en/papers/chung-2022-scaling-instruction-finetuned-language-models/)** *(2022 · 2,176 citations)* The authors from Google seek a way to teach pretrained language models to better handle tasks they have not seen before. ## Co-authors here 12 of 13 co-authors with a page, the most papers together first. - [Albert Webson](https://md.research.elamur.ai/free/en/authors/albert-webson/) - [Barret Zoph](https://md.research.elamur.ai/free/en/authors/barret-zoph/) - [Hyung Won Chung](https://md.research.elamur.ai/free/en/authors/hyung-won-chung/) - [Le Hou](https://md.research.elamur.ai/free/en/authors/le-hou/) - [Machel Reid](https://md.research.elamur.ai/free/en/authors/machel-reid/) - [Mostafa Dehghani](https://md.research.elamur.ai/free/en/authors/mostafa-dehghani/) - [Shayne Longpre](https://md.research.elamur.ai/free/en/authors/shayne-longpre/) - [Siddhartha Brahma](https://md.research.elamur.ai/free/en/authors/siddhartha-brahma/) - [William Fedus](https://md.research.elamur.ai/free/en/authors/william-fedus/) - [Xuezhi Wang](https://md.research.elamur.ai/free/en/authors/xuezhi-wang/) - [Yi Tay](https://md.research.elamur.ai/free/en/authors/yi-tay/) - [Yunxuan Li](https://md.research.elamur.ai/free/en/authors/yunxuan-li/) ## Cited by Papers of our library cite their works 4,399 times (of the 140,884 papers whose bibliographies we read); [307 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/shixiang-shane-gu/cited-by/).