Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models

TL;DR

This paper introduces an explainable multimodal meme detection method that uses large language models to generate conflicting rationales and fine-tunes a judge model for harm inference, improving detection accuracy and interpretability.

Contribution

The paper presents a novel multimodal debate-based approach leveraging LLMs for generating explanations and a fine-tuned judge model for harm detection in memes.

Findings

Outperforms state-of-the-art methods on three datasets

Provides interpretable explanations for harmfulness decisions

Demonstrates effective multimodal reasoning over memes

Abstract

The age of social media is flooded with Internet memes, necessitating a clear grasp and effective identification of harmful ones. This task presents a significant challenge due to the implicit meaning embedded in memes, which is not explicitly conveyed through the surface text and image. However, existing harmful meme detection methods do not present readable explanations that unveil such implicit meaning to support their detection decisions. In this paper, we propose an explainable approach to detect harmful memes, achieved through reasoning over conflicting rationales from both harmless and harmful positions. Specifically, inspired by the powerful capacity of Large Language Models (LLMs) on text generation and reasoning, we first elicit multimodal debate between LLMs to generate the explanations derived from the contradictory arguments. Then we propose to fine-tune a small language model as the debate judge for harmfulness inference, to facilitate multimodal fusion between the harmfulness rationales and the intrinsic multimodal information within memes. In this way, our model is empowered to perform dialectical reasoning over intricate and implicit harm-indicative patterns, utilizing multimodal explanations originating from both harmless and harmful arguments. Extensive experiments on three public meme datasets demonstrate that our harmful meme detection approach achieves much better performance than state-of-the-art methods and exhibits a superior capacity for explaining the meme harmfulness of the model predictions.