2.3k citations · 4.7k across the 88 of their papers we have counts for
7 papers · 2 filters
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought
Lionel Wong, Gabriel Grand, Alexander K. Lew +4
How does language inform our downstream thinking? In particular, how do humans make meaning from language--and how can we leverage a theory of linguistic meaning to build machines…
Understanding Social Reasoning in Language Models with Language Models
Kanishk Gandhi, Jan-Philipp Fränken, Tobias Gerstenberg +1
As Large Language Models (LLMs) become increasingly integrated into our everyday lives, understanding their ability to comprehend human mental states becomes critical for ensuring…
Characterizing tradeoffs between teaching via language and demonstrations in multi-agent systems
Dhara Yu, Noah D. Goodman, Jesse Mu
Humans teach others about the world through language and demonstration. When might one of these modalities be more effective than the other? In this work, we study the factors that…
Overinformative Question Answering by Humans and Machines
Polina Tsvilodub, Michael Franke, Robert D. Hawkins +1
When faced with a polar question, speakers often provide overinformative answers going beyond a simple "yes" or "no". But what principles guide the selection of additional informat…
Interpretability at Scale: Identifying Causal Mechanisms in Alpaca
Zhengxuan Wu, Atticus Geiger, Thomas Icard +2
Obtaining human-interpretable explanations of large, general-purpose language models is an urgent goal for AI safety. However, it is just as important that our interpretability met…
Solving Math Word Problems by Combining Language Models With Symbolic Solvers
Joy He-Yueya, Gabriel Poesia, Rose E. Wang +1
Automatically generating high-quality step-by-step solutions to math word problems has many applications in education. Recently, combining large language models (LLMs) with externa…