Search Results for author:
Found 4 papers, 1 papers with code
Decoding In-Context Learning: Neuroscience-inspired Analysis of Representations in Large Language Models
In this work, we investigate how LLM embeddings and attention representations change following in-context-learning, and how these changes mediate improvement in behavior.
ALLURE: Auditing and Improving LLM-based Evaluation of Text using Iterative In-Context-Learning
From grading papers to summarizing medical documents, large language models (LLMs) are evermore used for evaluation of text generated by humans and AI alike.
Discovering Distribution Shifts using Latent Space Representations
Rapid progress in representation learning has led to a proliferation of embedding models, and to associated challenges of model selection and practical application.
Graded persistence diagrams and persistence landscapes
The sum of the graded persistence diagrams is the persistence diagram.
Algebraic Topology 55N31, 06A07
