38 citations · 87 across the 9 of their papers we have counts for
6 papers · 1 filter
Learning adaptive planning representations with natural language guidance
Lionel Wong, Jiayuan Mao, Pratyusha Sharma +5
Effective planning in the real world requires not only world knowledge, but the ability to leverage that knowledge to build the right representation of the task at hand. Decades of…
LILO: Learning Interpretable Libraries by Compressing and Documenting Code
Gabriel Grand, Lionel Wong, Maddy Bowers +4
While large language models (LLMs) now excel at code generation, a key aspect of software development is the art of refactoring: consolidating code into libraries of reusable and r…
The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling Probabilistic Social Inferences from Linguistic Inputs
Lance Ying, Katherine M. Collins, Megan Wei +5
Human beings are social creatures. We routinely reason about other agents, and a crucial component of this social reasoning is inferring people's goals as we learn about their acti…
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…
Evaluating Language Models for Mathematics through Interactions
Katherine M. Collins, Albert Q. Jiang, Simon Frieder +11
There is much excitement about the opportunity to harness the power of large language models (LLMs) when building problem-solving assistants. However, the standard methodology of e…
Evaluating statistical language models as pragmatic reasoners
Benjamin Lipkin, Lionel Wong, Gabriel Grand +1
The relationship between communicated language and intended meaning is often probabilistic and sensitive to context. Numerous strategies attempt to estimate such a mapping, often l…