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About Adversarial Training For Fake News Classification
This document summarizes a research paper on using adversarial training to improve fake news classification. The paper trains two transformer models, BERT and Longformer, on fake news datasets using adversarial examples. Adversarial training helps models learn noise-invariant representations and improves robustness. The models are trained simultaneously on clean and perturbed examples. Results show adversarial training improves performance over the baseline models, increasing F1-score by up to 2.05% on political news datasets.
Detailed Information
| Author: | ['Karthik Yogesh'] |
|---|---|
| Publication Year: | 2024 |
| Pages: | 10 |
| Language: | English |
| Format: | |
| Price: | FREE |
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