activity
20212026
most citedGetting aligned on representational alignment

35 citations · 68 across the 26 of their papers we have counts for

collaborators
Showing 2024Show all

7 papers · 1 filter

cs.LG2024

Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning

Simon Frieder, Jonas Bayer, Sam Looi +13

The datasets and benchmarks commonly used to train and evaluate the mathematical capabilities of AI-based mathematical copilots (primarily large language models) exhibit several sh…

cs.LG2024★ 2 cited

Can Large Language Models Understand Symbolic Graphics Programs?

Zeju Qiu, Weiyang Liu, Haiwen Feng +7

Against the backdrop of enthusiasm for large language models (LLMs), there is a growing need to scientifically assess their capabilities and shortcomings. This is nontrivial in par…

cs.HC2024★ 2 cited

Building Machines that Learn and Think with People

Katherine M. Collins, Ilia Sucholutsky, Umang Bhatt +10

What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and t…

cs.GT2024

People use fast, goal-directed simulation to reason about novel games

Cedegao E. Zhang, Katherine M. Collins, Lionel Wong +3

People can evaluate features of problems and their potential solutions well before we can effectively solve them. When considering a game we have never played, for instance, we mig…

cs.HC2024★ 2 cited

Modulating Language Model Experiences through Frictions

Katherine M. Collins, Valerie Chen, Ilia Sucholutsky +6

Language models are transforming the ways that their users engage with the world. Despite impressive capabilities, over-consumption of language model outputs risks propagating unch…

cs.LG2024★ 3 cited

Large Language Models Must Be Taught to Know What They Don't Know

Sanyam Kapoor, Nate Gruver, Manley Roberts +7

When using large language models (LLMs) in high-stakes applications, we need to know when we can trust their predictions. Some works argue that prompting high-performance LLMs is s…

cs.LG2024★ 1 cited

Representational Alignment Supports Effective Machine Teaching

Ilia Sucholutsky, Katherine M. Collins, Maya Malaviya +11

A good teacher should not only be knowledgeable, but should also be able to communicate in a way that the student understands -- to share the student's representation of the world.…