3 citations · 4 across the 4 of their papers we have counts for
4 papers
LLM Confidence Evaluation Measures in Zero-Shot CSS Classification
David Farr, Iain Cruickshank, Nico Manzonelli +3
Assessing classification confidence is critical for leveraging large language models (LLMs) in automated labeling tasks, especially in the sensitive domains presented by Computatio…
LLM Chain Ensembles for Scalable and Accurate Data Annotation
David Farr, Nico Manzonelli, Iain Cruickshank +2
The ability of large language models (LLMs) to perform zero-shot classification makes them viable solutions for data annotation in rapidly evolving domains where quality labeled da…
RED-CT: A Systems Design Methodology for Using LLM-labeled Data to Train and Deploy Edge Classifiers for Computational Social Science
David Farr, Nico Manzonelli, Iain Cruickshank +1
Large language models (LLMs) have enhanced our ability to rapidly analyze and classify unstructured natural language data. However, concerns regarding cost, network limitations, an…
Membership Inference Attacks and Privacy in Topic Modeling
Nico Manzonelli, Wanrong Zhang, Salil Vadhan
Recent research shows that large language models are susceptible to privacy attacks that infer aspects of the training data. However, it is unclear if simpler generative models, li…