6 papers · 1 filter
Learning with Simulators: No Regret in a Computationally Bounded World
Sasha Voitovych, Abhishek Shetty, Noah Golowich +1
Understanding the minimal assumptions necessary for generalization is the fundamental question in learning theory. Unfortunately, most results rely heavily on independence (or some…
Subliminal Effects in Your Data: A General Mechanism via Log-Linearity
Ishaq Aden-Ali, Noah Golowich, Allen Liu +3
Training modern large language models (LLMs) has become a veritable smorgasbord of algorithms and datasets designed to elicit particular behaviors, making it critical to develop te…
Provably Learning from Modern Language Models via Low Logit Rank
Noah Golowich, Allen Liu, Abhishek Shetty
While modern language models and their inner workings are incredibly complex, recent work (Golowich, Liu & Shetty; 2025) has proposed a simple and potentially tractable abstraction…
Sequences of Logits Reveal the Low Rank Structure of Language Models
Noah Golowich, Allen Liu, Abhishek Shetty
A major problem in the study of large language models is to understand their inherent low-dimensional structure. We introduce an approach to study the low-dimensional structure of…
Taming Imperfect Process Verifiers: A Sampling Perspective on Backtracking
Dhruv Rohatgi, Abhishek Shetty, Donya Saless +4
Test-time algorithms that combine the generative power of language models with process verifiers that assess the quality of partial generations offer a promising lever for elicitin…
Omnipredictors for Regression and the Approximate Rank of Convex Functions
Parikshit Gopalan, Princewill Okoroafor, Prasad Raghavendra +2
Consider the supervised learning setting where the goal is to learn to predict labels given points from a distribution. An \textit{omnipredictor} for a clas…