RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Robust Fake News Detection
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914406663716864 |
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| author | Ma, Song-Duo Liu, Yi-Hung Lin, Hsin-Yu Chen, Pin-Yu Huang, Hong-Yan Hsu, Shau-Yung Chen, Yun-Nung |
| author_facet | Ma, Song-Duo Liu, Yi-Hung Lin, Hsin-Yu Chen, Pin-Yu Huang, Hong-Yan Hsu, Shau-Yung Chen, Yun-Nung |
| contents | To efficiently combat the spread of LLM-generated misinformation, we present RADAR, a Retrieval-Augmented Detector with Adversarial Refinement for robust fake news detection. Our approach employs a generator that rewrites real articles with factual perturbations, paired with a lightweight detector that verifies claims using dense passage retrieval. To enable effective co-evolution, we introduce verbal adversarial feedback (VAF). Rather than relying on scalar rewards, VAF issues structured natural-language critiques; these guide the generator toward more sophisticated evasion attempts, compelling the detector to adapt and improve. On a fake news detection benchmark, RADAR consistently outperforms strong retrieval-augmented trainable baselines, as well as general-purpose LLMs with retrieval. Further analysis shows that detector-side retrieval yields the largest gains, while VAF and few-shot demonstrations provide complementary benefits. RADAR also transfers better to fake news generated by an unseen external attacker, indicating improved robustness beyond the co-evolved training setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03981 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Robust Fake News Detection Ma, Song-Duo Liu, Yi-Hung Lin, Hsin-Yu Chen, Pin-Yu Huang, Hong-Yan Hsu, Shau-Yung Chen, Yun-Nung Computation and Language To efficiently combat the spread of LLM-generated misinformation, we present RADAR, a Retrieval-Augmented Detector with Adversarial Refinement for robust fake news detection. Our approach employs a generator that rewrites real articles with factual perturbations, paired with a lightweight detector that verifies claims using dense passage retrieval. To enable effective co-evolution, we introduce verbal adversarial feedback (VAF). Rather than relying on scalar rewards, VAF issues structured natural-language critiques; these guide the generator toward more sophisticated evasion attempts, compelling the detector to adapt and improve. On a fake news detection benchmark, RADAR consistently outperforms strong retrieval-augmented trainable baselines, as well as general-purpose LLMs with retrieval. Further analysis shows that detector-side retrieval yields the largest gains, while VAF and few-shot demonstrations provide complementary benefits. RADAR also transfers better to fake news generated by an unseen external attacker, indicating improved robustness beyond the co-evolved training setting. |
| title | RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Robust Fake News Detection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.03981 |