2 citations · 2 across the 12 of their papers we have counts for
13 papers
The Open Source Economic Index of AI Adoption and Capability
Seamus Somerstep, Aritra Guha, Divesh Srivastava +1
We work towards measuring both AI adoption and the capability of AI to perform discrete labor tasks across various occupations. To measure adoption, we develop an open-source econo…
A Latent Variable Framework for Scaling Laws in Large Language Models
Peiyao Cai, Chengyu Cui, Felipe Maia Polo +6
We propose a statistical framework built on latent variable modeling for scaling laws of large language models (LLMs). Our work is motivated by the rapid emergence of numerous new…
Limitations of refinement methods for weak to strong generalization
Seamus Somerstep, Ya'acov Ritov, Mikhail Yurochkin +2
Standard techniques for aligning large language models (LLMs) utilize human-produced data, which could limit the capability of any aligned LLM to human level. Label refinement and…
Learning to Choose or Choosing to Learn: Best-of-N vs. Supervised Fine-Tuning for Bit String Generation
Seamus Somerstep, Vinod Raman, Unique Subedi +1
Using the bit string generation problem as a case study, we theoretically compare two standard methods for adapting large language models to new tasks. The first, referred to as su…
CARROT: A Cost Aware Rate Optimal Router
Seamus Somerstep, Felipe Maia Polo, Allysson Flavio Melo de Oliveira +5
With the rapid growth in the number of Large Language Models (LLMs), there has been a recent interest in LLM routing, or directing queries to the cheapest LLM that can deliver a su…
Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families
Felipe Maia Polo, Seamus Somerstep, Leshem Choshen +2
Scaling laws for large language models (LLMs) predict model performance based on parameters like size and training data. However, differences in training configurations and data pr…
Microfoundation Inference for Strategic Prediction
Daniele Bracale, Subha Maity, Felipe Maia Polo +3
Often in prediction tasks, the predictive model itself can influence the distribution of the target variable, a phenomenon termed performative prediction. Generally, this influence…
Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness
Seamus Somerstep, Ya'acov Ritov, Yuekai Sun
In many prediction problems, the predictive model affects the distribution of the prediction target. This phenomenon is known as performativity and is often caused by the behavior…
A transfer learning framework for weak-to-strong generalization
Seamus Somerstep, Felipe Maia Polo, Moulinath Banerjee +3
Modern large language model (LLM) alignment techniques rely on human feedback, but it is unclear whether these techniques fundamentally limit the capabilities of aligned LLMs. In p…
Learning In Reverse Causal Strategic Environments With Ramifications on Two Sided Markets
Seamus Somerstep, Yuekai Sun, Ya'acov Ritov
Motivated by equilibrium models of labor markets, we develop a formulation of causal strategic classification in which strategic agents can directly manipulate their outcomes. As a…
All the 's on cyclic admissible covers
Renzo Cavalieri, Bryson Owens, Seamus Somerstep
We compute the degree of Hurwitz-Hodge classes on one dimensional moduli spaces of cyclic admissible covers of the projective line. We also compute the degree of the the fi…
Boundary Expression for Chern Classes of the Hodge Bundle on Spaces of Cyclic Covers
Bryson Owens, Seamus Somerstep
We compute an explicit formula for the first Chern class of the Hodge Bundle over the space of admissible cyclic covers of -pointed rational stable curv…
The Supersingularity of Hurwitz Curves
Dean Bisogno, Erin Dawson, Henry Frauenhoff +5
We study when Hurwitz curves are supersingular. Specifically, we show that the curve , with and relatively prime, is s…