7 papers · 1 filter
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…
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…