50 citations · 196 across the 15 of their papers we have counts for
4 papers · 1 filter
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…
Co-training Improves Prompt-based Learning for Large Language Models
Hunter Lang, Monica Agrawal, Yoon Kim +1
We demonstrate that co-training (Blum & Mitchell, 1998) can improve the performance of prompt-based learning by using unlabeled data. While prompting has emerged as a promising par…