53 citations · 66 across the 18 of their papers we have counts for
9 papers · 1 filter
Learning User Simulators with Turing Rewards
Yingshan Susan Wang, Cedegao E. Zhang, Linlu Qiu +5
Learning to simulate human users in interactive settings could advance the training of agent assistants, evaluation of personalization systems, research in the social sciences, and…
Code-enabled language models can outperform reasoning models on diverse tasks
Cedegao E. Zhang, Cédric Colas, Gabriel Poesia +2
Reasoning models (RMs), language models (LMs) trained with reinforcement learning to produce long-form natural language reasoning, have been remarkably successful, but they still r…
Evaluating Language Models' Evaluations of Games
Katherine M. Collins, Cedegao E. Zhang, Graham Todd +9
Reasoning is not just about solving problems -- it is also about evaluating which problems are worth solving at all. Evaluations of artificial intelligence (AI) systems primarily f…
On the Same Wavelength? Evaluating Pragmatic Reasoning in Language Models across Broad Concepts
Linlu Qiu, Cedegao E. Zhang, Joshua B. Tenenbaum +2
Language use is shaped by pragmatics -- i.e., reasoning about communicative goals and norms in context. As language models (LMs) are increasingly used as conversational agents, it…
Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models
Lionel Wong, Katherine M. Collins, Lance Ying +8
When faced with novel situations, people are able to marshal relevant considerations from a wide range of background knowledge and put these to use in inferences and predictions. W…
Language-Informed Synthesis of Rational Agent Models for Grounded Theory-of-Mind Reasoning On-The-Fly
Lance Ying, Ryan Truong, Katherine M. Collins +6
Drawing real world social inferences usually requires taking into account information from multiple modalities. Language is a particularly powerful source of information in social…
Scaling up the think-aloud method
Daniel Wurgaft, Ben Prystawski, Kanishk Gandhi +3
The think-aloud method, where participants voice their thoughts as they solve a task, is a valuable source of rich data about human reasoning processes. Yet, it has declined in pop…
Conditional and Modal Reasoning in Large Language Models
Wesley H. Holliday, Matthew Mandelkern, Cedegao E. Zhang
The reasoning abilities of large language models (LLMs) are the topic of a growing body of research in AI and cognitive science. In this paper, we probe the extent to which twenty-…
LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers
Theo X. Olausson, Alex Gu, Benjamin Lipkin +4
Logical reasoning, i.e., deductively inferring the truth value of a conclusion from a set of premises, is an important task for artificial intelligence with wide potential impacts…