Distinguishing Scams and Fraud with Ensemble Learning
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Abstract (arXiv)
Users increasingly query LLM-enabled web chatbots for help with scam defense. The Consumer Financial Protection Bureau's complaints database is a rich data source for evaluating LLM performance on user scam queries, but currently the corpus does not distinguish between scam and non-scam fraud. We developed an LLM ensemble approach to distinguishing scam and fraud CFPB complaints and describe initial findings regarding the strengths and weaknesses of LLMs in the scam defense context.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 27 references that are in the map. First posted on arXiv 11 Dec 2024.
Builds on
6 of 27 references.
References · 6
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2020Extracting Training Data from Large Language Modelscited by 2,144
- 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 428
- 2024Large Language Models for Data Annotation and Synthesis: A Surveycited by 201
- 2024Combating Phone Scams with LLM-based Detection: Where Do We Stand?cited by 10
- 2024Can LLMs be Scammed? A Baseline Measurement Studycited by 3
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Citing papers per year: 2025: 1.
1 paper.