Papers with pairwise comparative reasoning strategy
ICLAD: In-Context Learning with Comparison-Guidance for Audio Deepfake Detection (2026.findings-acl)
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| Challenge: | Audio deepfake detection systems do not generalize well to realistic in-the-wild deepfakkes. |
| Approach: | They propose a novel In-Context Learning paradigm with comparison-guidance for Audio Deepfake detection framework that uses audio language models for training-free generalization to unseen deepfakes. |
| Outcome: | The proposed framework improves macro F1 over specialized detectors on in-the-wild datasets with up to 2 relative improvement over existing models. |