Lucas Dixon

Lucas Dixon

Lucas is a principal research scientist in Google DeepMind and co-lead of PAIR (People and AI Research). He works on visualisation, interpretability and control of machine learning systems, and specifically language models. His work explores how people can productively and fairly benefit from machine learning systems.

Previously, he was Chief Scientist at Jigsaw where he founded engineering and research. He has contributed scientific advances and systems in multiple disciplines including digital security, formal logic, machine learning, and data visualization. For example he co-founded uProxy & Outline, Project Shield, DigitalAttackMap; Syria Defection Tracker, unfiltered.news, Conversation AI and Perspective API.

Before Google, Lucas completed his PhD and worked at the University of Edinburgh on the automation of mathematical reasoning and graphical languages applied to quantum information. He also helped run a non-profit working towards more rational and informed discussion and decision making, and was a co-founder of TheoryMine - a playful take on automating mathematical discovery. Outside of scientific advances, Lucas is also a martial arts instructor in Paris.

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LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models
Minsuk Kahng
Ian Tenney
Mahima Pushkarna
Michael Xieyang Liu
James Wexler
Emily Reif
Krystal Kallarackal
Minsuk Chang
Michael Terry
Lucas Dixon
Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '24), ACM (2024)
"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output
Michael Xieyang Liu
Frederick Liu
Alex Fiannaca
Terry Koo
Lucas Dixon
Michael Terry
Carrie Cai
In Extended Abstract in ACM CHI Conference on Human Factors in Computing Systems (CHI EA '24), ACM (2024), pp. 9 (to appear)
Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences
Scott Sanner
Krisztian Balog
Filip Radlinski
Ben Wedin
Lucas Dixon
Proceedings of ACM Conference on Recommender Systems (RecSys ’23) (2023) (to appear)
On Natural Language User Profiles for Transparent and Scrutable Recommendation
Filip Radlinski
Krisztian Balog
Fernando Diaz
Lucas Gill Dixon
Ben Wedin
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22) (2022)
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis
Shayegan Omidshafiei
Andrei Kapishnikov
Yannick Assogba
Lucas Gill Dixon
Been Kim
Advances in Neural Information Processing Systems (NeurIPS) (2022) (to appear)
Sparsely Activated Language Models are Efficient In-Context Learners
Adams Yu
Andrew Dai
Barret Richard Zoph
Claire Cui
Dmitry (Dima) Lepikhin
Emma Wang
Kathy Meier-Hellstern
Kellie Webster
Kevin Robinson
Kun Zhang
Liam B. Fedus
Lucas Dixon
Maarten Paul Bosma
Marie Pellat
Maxim Krikun
Nan Du
Orhan Firat
Quoc V. Le
Simon Tong
Tao Wang
Toju Duke
Yanping Huang
Yanqi Zhou
Yonghui Wu
Yuanzhong Xu
Zhifeng Chen
Zongwei Zhou
(2022)
Context Sensitivity Estimation in Toxicity Detection
Alexandros Xenos
Ioannis Pavlopoulos
Ion Androutsopoulos
Jeffrey Scott Sorensen
Lucas Gill Dixon
First Monday (2022)
Context-Sensitivity Estimation in Toxicity Classification
Ioannis Pavlopoulos
Ion Androutsopoulos
Jeffrey Scott Sorensen
Lucas Gill Dixon
ACL-IJCNLP 2021: (2021)
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
Daniel Borkan
Jeffrey Sorensen
Lucas Dixon
Lucy Vasserman
Nithum Thain
ACM Conference on Fairness, Accountability, and Transparency (2019) (to appear)
Conversations Gone Awry: Detecting Warning Signs of Conversational Failure
Justine Zhang
Jonathan P. Chang
Cristian Danescu-Niculescu-Mizil
Lucas Dixon
Nithum Thain
Dario Taraborelli
Proceedings of ACL, ACM Digital Library (2018)
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