50 citations · 196 across the 15 of their papers we have counts for
15 papers
Theoretical Analysis of Weak-to-Strong Generalization
Hunter Lang, David Sontag, Aravindan Vijayaraghavan
Strong student models can learn from weaker teachers: when trained on the predictions of a weaker model, a strong pretrained student can learn to correct the weak model's errors an…
Learning to Decode Collaboratively with Multiple Language Models
Shannon Zejiang Shen, Hunter Lang, Bailin Wang +2
We propose a method to teach multiple large language models (LLM) to collaborate by interleaving their generations at the token level. We model the decision of which LLM generates…
Who Should Predict? Exact Algorithms For Learning to Defer to Humans
Hussein Mozannar, Hunter Lang, Dennis Wei +3
Automated AI classifiers should be able to defer the prediction to a human decision maker to ensure more accurate predictions. In this work, we jointly train a classifier with a re…
TabLLM: Few-shot Classification of Tabular Data with Large Language Models
Stefan Hegselmann, Alejandro Buendia, Hunter Lang +3
We study the application of large language models to zero-shot and few-shot classification of tabular data. We prompt the large language model with a serialization of the tabular d…
Training Subset Selection for Weak Supervision
Hunter Lang, Aravindan Vijayaraghavan, David Sontag
Existing weak supervision approaches use all the data covered by weak signals to train a classifier. We show both theoretically and empirically that this is not always optimal. Int…
Large Language Models are Few-Shot Clinical Information Extractors
Monica Agrawal, Stefan Hegselmann, Hunter Lang +2
A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes. However, roadblocks have included dataset shift from the gener…