Search Results for author:
Found 2 papers, 1 papers with code
Learning from Limited Labels for Long Legal Dialogue
We study four representative tasks from the parole dataset.
Detecting Label Errors by using Pre-Trained Language Models
We show that large pre-trained language models are inherently highly capable of identifying label errors in natural language datasets: simply examining out-of-sample data points in descending order of fine-tuned task loss significantly outperforms more complex error-detection mechanisms proposed in previous work.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
