2 citations · 4 across the 9 of their papers we have counts for
5 papers · 1 filter
Personalized Recommendations Without Inducing Congestion: Mitigating Disparities in the NYC High School Match
Erica Chiang, Kenny Peng, Rebecca Lichtenstein +7
Algorithmic recommendations can help participants navigate large matching markets. For example, recommendations for school and college choices may reduce information frictions and…
A Testable Theory of Atomic Features
Kenny Peng, Jon Kleinberg, Nikhil Garg
We develop and test a theory of language model representations in which there exist atomic features. Our main theoretical insight is that in such a model, sparse dictionaries (e.g.…
Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift
Kevin Ren, Manish Raghavan, Nikhil Garg
Deployed AI text detection approaches are supervised, relying on training-time access to labeled datasets of both human-written and AI-generated text. This approach is vulnerable t…
The Subjectivity of Monoculture
Nathanael Jo, Nikhil Garg, Manish Raghavan
Machine learning models---including large language models (LLMs)---are often said to exhibit monoculture, where outputs agree strikingly often. However, we present two experiments…
How Many Features Can a Language Model Store Under the Linear Representation Hypothesis?
Nikhil Garg, Jon Kleinberg, Kenny Peng
We introduce a mathematical framework for the linear representation hypothesis (LRH), which asserts that intermediate layers of language models store features linearly. We separate…