---
title: "Geoffrey Irving — author profile and papers"
description: "Geoffrey Irving, The University of Texas at Austin: 3 papers on Elamur Research (2022), h-index 29 — affiliations, topics, co-authors and who cites them…"
canonical: "https://research.elamur.ai/free/en/authors/geoffrey-irving/"
---
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# Geoffrey Irving

3 papers on this site · 2022

current affiliation [The University of Texas at Austin](https://ror.org/00hj54h04), Austin, United States

Geoffrey Irving has 3 papers on this site, 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/), [Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/); often with [Francis Song](https://md.research.elamur.ai/free/en/authors/francis-song/), [John Aslanides](https://md.research.elamur.ai/free/en/authors/john-aslanides/), [Nat McAleese](https://md.research.elamur.ai/free/en/authors/nat-mcaleese/).

[OpenAlex](https://openalex.org/A5029611587) · [**h-index 29**](https://openalex.org/authors/A5029611587) · [48 works](https://openalex.org/works?filter=author.id:A5029611587) · [25 993 citations](https://openalex.org/works?filter=author.id:A5029611587&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[3 works on this site](https://md.research.elamur.ai/free/en/authors/geoffrey-irving/#papers) · [4 903 citations in our library](https://md.research.elamur.ai/free/en/authors/geoffrey-irving/cited-by/) · 6 more works in our library

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=Geoffrey+Irving) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Geoffrey+Irving) · [dblp](https://dblp.org/search?q=Geoffrey+Irving) · [ACL Anthology](https://aclanthology.org/search/?q=Geoffrey+Irving)

## Affiliations

- [The University of Texas at Austin](https://ror.org/00hj54h04) · Austin, United States —current · 2025 on 1 paper
- [Google DeepMind (United Kingdom)](https://ror.org/00971b260) · London, United Kingdom —2022 on 3 papers
- [New York University](https://ror.org/0190ak572) · New York, United States —2022 on 2 papers
- [Google (United States)](https://ror.org/00njsd438) · Mountain View, United States —2016–2021 on 6 papers
- [Otherlab](https://ror.org/04wxq1e36) · San Francisco, United States —2013 on 2 papers
- [Stanford University](https://ror.org/00f54p054) · Stanford, United States —2004–2008 on 10 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 & pretraining 2](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning 1](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Bias, fairness & safety 1](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/)

## Papers

The most cited first (OpenAlex and our library).

1. **[Red Teaming Language Models with Language Models](https://md.research.elamur.ai/free/en/papers/perez-2022-red-teaming-language-models-language/)** *(2022 · 665 citations)* Safety testing of language models before release usually relies on people hand-writing probing inputs. This is costly and covers only a narrow slice of possible failures. Perez et al. automate this "red teaming" in three steps: a red LM generates test questions, the target LM answers them, and a classifier flags…
2. **[Solving math word problems with process- and outcome-based feedback](https://md.research.elamur.ai/free/en/papers/uesato-2022-solving-math-word-problems-process/)** *(2022 · 334 citations)* Asking language models to write out step-by-step reasoning makes them better at reasoning tasks. Once you fine-tune instead of only prompting, though, you have to decide what to reward: just the final answer (outcome-based supervision) or every intermediate step (process-based supervision). The DeepMind team behind…
3. **[Teaching language models to support answers with verified quotes](https://md.research.elamur.ai/free/en/papers/menick-2022-teaching-language-models-support-answers/)** *(2022 · 211 citations)* Large language models frequently get fact-based queries right, but users cannot tell which claims to trust without checking them, because models can invent convincing falsehoods.

## Also in our library

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

## Co-authors here

12 of 15 co-authors with a page, the most papers together first.

- [Francis Song](https://md.research.elamur.ai/free/en/authors/francis-song/) ×3
- [John Aslanides](https://md.research.elamur.ai/free/en/authors/john-aslanides/) ×2
- [Nat McAleese](https://md.research.elamur.ai/free/en/authors/nat-mcaleese/) ×2
- [Amelia Glaese](https://md.research.elamur.ai/free/en/authors/amelia-glaese/)
- [Antonia Creswell](https://md.research.elamur.ai/free/en/authors/antonia-creswell/)
- [Ethan Perez](https://md.research.elamur.ai/free/en/authors/ethan-perez/)
- [Jacob Menick](https://md.research.elamur.ai/free/en/authors/jacob-menick/)
- [Jonathan Uesato](https://md.research.elamur.ai/free/en/authors/jonathan-uesato/)
- [Lisa Wang](https://md.research.elamur.ai/free/en/authors/lisa-wang/)
- [Lucy Campbell-Gillingham](https://md.research.elamur.ai/free/en/authors/lucy-campbell-gillingham/)
- [Maja Trebacz](https://md.research.elamur.ai/free/en/authors/maja-trebacz/)
- [Martin Chadwick](https://md.research.elamur.ai/free/en/authors/martin-chadwick/)

## Cited by

Papers of our library cite their works 4 903 times (of the 140 884 papers whose bibliographies we read); [325 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/geoffrey-irving/cited-by/).
