🧑‍🔬 Joel Hestness

Affiliations

🏢Cerebras SystemsSunnyvale2019–2026 · 12 paperscurrent🏢BaiduBeijing · China2017–2019 · 6 papers🎓The University of Texas at AustinAustin · United States2010–2012 · 6 papers🎓University of Wisconsin–MadisonMadison · United States2014–2015 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

Topics

🧠 Language models & pretraining1

Works by year

2020s

2010s

Papers

The most cited first (OpenAlex and our library).

1. Deep Learning Scaling is Predictable, Empirically (2017 · 497 citations)
   Practitioners widely assume that more data and bigger models improve accuracy, but there was no reliable way to forecast how much gain a given increase in training data would bring.
2. Bilingual Adaptation of Monolingual Foundation Models (2024 · 7 citations)
   English-dominant open LLMs such as Llama 2 handle other languages poorly. Training a bilingual model from scratch is costly and tends to give weaker English. Adapting an existing model brings its own problems: it can forget English, and its English-centric tokenizer splits foreign text into many pieces. The authors…

Also in our library

2 more works of theirs are in our library without a page here yet; they will appear as we publish them.

Co-authors here

12 co-authors with a page, the most papers together first.

• Biswajit Mishra
• Bokang Jia
• Greg Diamos
• Gurpreet Gosal
• Heewoo Jun
• Neha Sengupta
• Rituraj Joshi
• Sharan Narang
• Sunil Kumar Sahu
• Yang Yang
• Yanqi Zhou
• Zhiming Chen

Cited by

Papers of our library cite their works 200 times (of the 140,884 papers whose bibliographies we read); 42 citing papers with a page here →.

This page in Markdown → https://md.research.elamur.ai/free/en/authors/joel-hestness/