Neil Tenenholtz — accepted AI-conference papers

2024

Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains

Junhong Shen, Neil Tenenholtz, James Brian Hall, David Alvarez-Melis, Nicolo Fusi

ICML 2024poster

Large Language Models (LLMs) have demonstrated remarkable proficiency in understanding and generating natural language. However, their capabilities wane in highly specialized domains underrepresented in the pretraining corpus, such as physical and biomedical sciences. This work explores how to repur…

Cited by 29 Source ↗PDF ↗code ↗Scholar ↗

2023

Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations

Hammaad Adam, Fan Yin, Mary Hu, Neil Tenenholtz, Lorin Crawford, Lester Mackey +1

NeurIPS 2023spotlight

Randomized experiments often need to be stopped prematurely due to the treatment having an unintended harmful effect. Existing methods that determine when to stop an experiment early are typically applied to the data in aggregate and do not account for treatment effect heterogeneity. In this paper,…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗