A progressive two-stage knowledge distillation framework (HKD4VLM) reports first-place F1 scores of 98.2% and 98.4% on multimodal hallucination and factuality detection, but its ablation lacks a directly fine-tuned baseline.
A Deep Learning Approach for Automatic Detection of Fake News
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abstract
Fake news detection is a very prominent and essential task in the field of journalism. This challenging problem is seen so far in the field of politics, but it could be even more challenging when it is to be determined in the multi-domain platform. In this paper, we propose two effective models based on deep learning for solving fake news detection problem in online news contents of multiple domains. We evaluate our techniques on the two recently released datasets, namely FakeNews AMT and Celebrity for fake news detection. The proposed systems yield encouraging performance, outperforming the current handcrafted feature engineering based state-of-the-art system with a significant margin of 3.08% and 9.3% by the two models, respectively. In order to exploit the datasets, available for the related tasks, we perform cross-domain analysis (i.e. model trained on FakeNews AMT and tested on Celebrity and vice versa) to explore the applicability of our systems across the domains.
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HKD4VLM: A Progressive Hybrid Knowledge Distillation Framework for Robust Multimodal Hallucination and Factuality Detection in VLMs
A progressive two-stage knowledge distillation framework (HKD4VLM) reports first-place F1 scores of 98.2% and 98.4% on multimodal hallucination and factuality detection, but its ablation lacks a directly fine-tuned baseline.