An ensemble of GPT-4 and Gemini prompts, fitted to 300 hand-labeled CFPB complaints, labels scam vs. non-scam fraud with reported precision .95/recall .84 on the same training set and precision .97 on a 133-complaint sample.
The science of persuasion
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CR 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Distinguishing Scams and Fraud with Ensemble Learning
An ensemble of GPT-4 and Gemini prompts, fitted to 300 hand-labeled CFPB complaints, labels scam vs. non-scam fraud with reported precision .95/recall .84 on the same training set and precision .97 on a 133-complaint sample.