Embers of autoregression show how large language models are shaped by the problem they are trained to solve
Tom McCoy, Shunyu Yao, Dan Friedman, Matt Hardy, and Tom Griffiths (2024). PNAS.
When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1
Tom McCoy, Shunyu Yao, Dan Friedman, Matt Hardy, and Tom Griffiths (2024). arXiv preprint.
Improving out-of-population prediction: The complementary effects of model assistance and judgmental bootstrapping
Matt Hardy, Sam Zhang, Jessica Hullman, Jake Hofman, and Dan Goldstein (2024). International Journal of Forecasting.
AI-generated visuals of car-free American cities help increase support for sustainable transport policies
Rachit Dubey, Matt Hardy, Tom Griffiths, and Rahul Bhui (2024). Nature Sustainability.
Resampling reduces bias amplification in experimental social networks
Matt Hardy, Bill Thompson, P.M. Krafft, and Tom Griffiths (2023). Nature Human Behaviour.
Optimal nudging for cognitively bounded agents: A framework for modeling,
predicting, and controlling the effects of choice architectures
Fred Callaway, Matt Hardy, and Tom Griffiths (2023). Psychological Review.
How do humans overcome individual computational limitations by working together?
Natalia VĂ©lez, Brian Christian, Matt Hardy, Bill Thompson, and Tom Griffiths (2023).
Cognitive Science.
Overcoming individual limitations through distributed computation: Rational information
accumulation in multi-generational populations
Matt Hardy, P.M. Krafft, Bill Thompson, and Tom Griffiths (2022).
Topics in Cognitive Science.