LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation

TL;DR

This paper introduces LEMMA, a method that enhances multimodal misinformation detection by augmenting LVLMs with external knowledge, significantly improving accuracy on social media datasets.

Contribution

The paper proposes a novel external knowledge augmentation technique for LVLMs to improve multimodal misinformation detection accuracy.

Findings

LEMMA improves detection accuracy by 7% on Twitter dataset.

LEMMA improves detection accuracy by 13% on Fakeddit dataset.

LVLMs have strong reasoning skills but need external knowledge for better verification.

Abstract

The rise of multimodal misinformation on social platforms poses significant challenges for individuals and societies. Its increased credibility and broader impact compared to textual misinformation make detection complex, requiring robust reasoning across diverse media types and profound knowledge for accurate verification. The emergence of Large Vision Language Model (LVLM) offers a potential solution to this problem. Leveraging their proficiency in processing visual and textual information, LVLM demonstrates promising capabilities in recognizing complex information and exhibiting strong reasoning skills. In this paper, we first investigate the potential of LVLM on multimodal misinformation detection. We find that even though LVLM has a superior performance compared to LLMs, its profound reasoning may present limited power with a lack of evidence. Based on these observations, we propose LEMMA: LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation. LEMMA leverages LVLM intuition and reasoning capabilities while augmenting them with external knowledge to enhance the accuracy of misinformation detection. Our method improves the accuracy over the top baseline LVLM by 7% and 13% on Twitter and Fakeddit datasets respectively.