Academic

LLM-MRD: LLM-Guided Multi-View Reasoning Distillation for Fake News Detection

arXiv:2603.19293v1 Announce Type: new Abstract: Multimodal fake news detection is crucial for mitigating societal disinformation. Existing approaches attempt to address this by fusing multimodal features or leveraging Large Language Models (LLMs) for advanced reasoning. However, these methods suffer from serious limitations, including a lack of comprehensive multi-view judgment and fusion, and prohibitive reasoning inefficiency due to the high computational costs of LLMs. To address these issues, we propose \textbf{LLM}-Guided \textbf{M}ulti-View \textbf{R}easoning \textbf{D}istillation for Fake News Detection ( \textbf{LLM-MRD}), a novel teacher-student framework. The Student Multi-view Reasoning module first constructs a comprehensive foundation from textual, visual, and cross-modal perspectives. Then, the Teacher Multi-view Reasoning module generates deep reasoning chains as rich supervision signals. Our core Calibration Distillation mechanism efficiently distills this complex reas

W
Weilin Zhou, Shanwen Tan, Enhao Gu, Yurong Qian · · 1 min read · 7 views
#cs.CL #cs.AI

arXiv:2603.19293v1 Announce Type: new Abstract: Multimodal fake news detection is crucial for mitigating societal disinformation. Existing approaches attempt to address this by fusing multimodal features or leveraging Large Language Models (LLMs) for advanced reasoning. However, these methods suffer from serious limitations, including a lack of comprehensive multi-view judgment and fusion, and prohibitive reasoning inefficiency due to the high computational costs of LLMs. To address these issues, we propose \textbf{LLM}-Guided \textbf{M}ulti-View \textbf{R}easoning \textbf{D}istillation for Fake News Detection ( \textbf{LLM-MRD}), a novel teacher-student framework. The Student Multi-view Reasoning module first constructs a comprehensive foundation from textual, visual, and cross-modal perspectives. Then, the Teacher Multi-view Reasoning module generates deep reasoning chains as rich supervision signals. Our core Calibration Distillation mechanism efficiently distills this complex reasoning-derived knowledge into the efficient student model. Experiments show LLM-MRD significantly outperforms state-of-the-art baselines. Notably, it demonstrates a comprehensive average improvement of 5.19\% in ACC and 6.33\% in F1-Fake when evaluated across all competing methods and datasets. Our code is available at https://github.com/Nasuro55/LLM-MRD

Executive Summary

This article proposes LLM-MRD, a novel teacher-student framework for multimodal fake news detection. By integrating Large Language Models (LLMs) with multi-view reasoning, LLM-MRD aims to address the limitations of existing approaches. The framework constructs a comprehensive foundation from textual, visual, and cross-modal perspectives, generates deep reasoning chains, and distills complex reasoning-derived knowledge into an efficient student model. Experiments demonstrate significant improvements over state-of-the-art baselines, with an average improvement of 5.19% in ACC and 6.33% in F1-Fake across all competing methods and datasets. The proposed framework has the potential to mitigate societal disinformation and may be applied in various domains where multimodal information is present.

Key Points

  • ▸ LLM-MRD is a novel teacher-student framework for multimodal fake news detection
  • ▸ The framework integrates LLMs with multi-view reasoning to address existing limitations
  • ▸ Experiments demonstrate significant improvements over state-of-the-art baselines

Merits

Comprehensive multi-view judgment and fusion

LLM-MRD constructs a comprehensive foundation from textual, visual, and cross-modal perspectives, addressing the lack of comprehensive multi-view judgment and fusion in existing approaches.

Efficient reasoning and knowledge distillation

The Calibration Distillation mechanism efficiently distills complex reasoning-derived knowledge into the efficient student model, addressing the prohibitive reasoning inefficiency of high computational costs of LLMs.

Demerits

Computational costs and scalability

While the Calibration Distillation mechanism addresses the prohibitive reasoning inefficiency of high computational costs of LLMs, the overall computational costs and scalability of the proposed framework remain a concern, particularly for large-scale applications.

Limited evaluation on real-world datasets

The experiments presented in the article are primarily conducted on benchmark datasets, and it is unclear how well LLM-MRD performs on real-world datasets with diverse and complex multimodal information.

Expert Commentary

The article presents a novel and promising approach to multimodal fake news detection, integrating LLMs with multi-view reasoning to address the limitations of existing approaches. While the proposed framework demonstrates significant improvements over state-of-the-art baselines, further research is needed to address the computational costs and scalability concerns, as well as to evaluate its performance on real-world datasets with diverse and complex multimodal information. The implications of LLM-MRD are significant, particularly in the context of social media and online platforms, and its applications may extend beyond fake news detection to other domains where multimodal information is present.

Recommendations

  • ✓ Future research should focus on addressing the computational costs and scalability concerns of the proposed framework, potentially through the use of more efficient LLMs or parallel processing techniques.
  • ✓ The authors should conduct further experiments on real-world datasets with diverse and complex multimodal information to evaluate the robustness and generalizability of LLM-MRD.

Sources

Original: arXiv - cs.CL

Related Articles