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Progress Measures for Grokking on Real-world Datasets
Grokking, a phenomenon where machine learning models generalize long after overfitting, has been primarily observed and studied in algorithmic tasks.
NICE: To Optimize In-Context Examples or Not?
Recent work shows that in-context learning and optimization of in-context examples (ICE) can significantly improve the accuracy of large language models (LLMs) on a wide range of tasks, leading to an apparent consensus that ICE optimization is crucial for better performance.
CataractBot: An LLM-Powered Expert-in-the-Loop Chatbot for Cataract Patients
The healthcare landscape is evolving, with patients seeking more reliable information about their health conditions, treatment options, and potential risks.
Position Paper: Toward New Frameworks for Studying Model Representations
Mechanistic interpretability (MI) aims to understand AI models by reverse-engineering the exact algorithms neural networks learn.
Predicting Treatment Adherence of Tuberculosis Patients at Scale
While TB is treatable, non-adherence to the medication regimen is a significant cause of morbidity and mortality.
