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Found 6 papers, 3 papers with code
Convex Optimization in Julia
This paper describes Convex, a convex optimization modeling framework in Julia.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
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.
Challenges for Information Extraction from Dialogue in Criminal Law
Information extraction and question answering have the potential to introduce a new paradigm for how machine learning is applied to criminal law.
Learning from Limited Labels for Long Legal Dialogue
We study four representative tasks from the parole dataset.
Unsupervised Anomaly Detection in Parole Hearings using Language Models
We present quantitative analysis of the results and note that our method has identified some important cases for review.
