Multi-MLLM Knowledge Distillation for Out-of-Context News Detection

• URL: http://arxiv.org/abs/2505.22517v1
• Date: Wed, 28 May 2025 16:03:41 GMT
• Title: Multi-MLLM Knowledge Distillation for Out-of-Context News Detection
• Authors: Yimeng Gu, Zhao Tong, Ignacio Castro, Shu Wu, Gareth Tyson,
• Abstract summary: Multimodal out-of-context news is a type of misinformation in which the image is used outside of its original context.<n>We introduce a two-stage knowledge distillation framework to transfer this knowledge to a student MLLM.<n>In Stage 1, we apply LoRA fine-tuning to the student model using all training data.<n>In Stage 2, we further fine-tune the student model using both LoRA fine-tuning and DPO on the data points where teachers' predictions conflict.
• Score: 17.41734069411864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal out-of-context news is a type of misinformation in which the image is used outside of its original context. Many existing works have leveraged multimodal large language models (MLLMs) for detecting out-of-context news. However, observing the limited zero-shot performance of smaller MLLMs, they generally require label-rich fine-tuning and/or expensive API calls to GPT models to improve the performance, which is impractical in low-resource scenarios. In contrast, we aim to improve the performance of small MLLMs in a more label-efficient and cost-effective manner. To this end, we first prompt multiple teacher MLLMs to generate both label predictions and corresponding rationales, which collectively serve as the teachers' knowledge. We then introduce a two-stage knowledge distillation framework to transfer this knowledge to a student MLLM. In Stage 1, we apply LoRA fine-tuning to the student model using all training data. In Stage 2, we further fine-tune the student model using both LoRA fine-tuning and DPO on the data points where teachers' predictions conflict. This two-stage strategy reduces annotation costs and helps the student model uncover subtle patterns in more challenging cases. Experimental results demonstrate that our approach achieves state-of-the-art performance using less than 10% labeled data.

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