Distinguishing Scams and Fraud with Ensemble Learning
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910742033203200 |
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| author | Chadalavada, Isha Huang, Tianhui Staddon, Jessica |
| author_facet | Chadalavada, Isha Huang, Tianhui Staddon, Jessica |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08680 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Distinguishing Scams and Fraud with Ensemble Learning Chadalavada, Isha Huang, Tianhui Staddon, Jessica Cryptography and Security Artificial Intelligence Human-Computer Interaction Machine Learning 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. |
| title | Distinguishing Scams and Fraud with Ensemble Learning |
| topic | Cryptography and Security Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2412.08680 |