LLMs for Explainable Few-shot Deception Detection
Author:
Affiliation:
1. Dept. of Computer Science, University of Houston, Houston, TX, USA
Funder
National Science Foundation
Army Research Office
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3643651.3659898
Reference61 articles.
1. Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques
2. Contributions to the study of SMS spam filtering
3. Dainis Boumber, Fatima Zahra Qachfar, and Rakesh M Verma. 2024. Domain- Agnostic Adapter Architecture for Deception Detection: Extensive Evaluations with the DIFrauD Benchmark. In Joint International Conference on Computational Linguistics, Language Resources and Evaluation. European Language Resources Association, Torino, Italy.
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