Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
This addresses the problem of implicit meaning in harmful memes for social media moderation by providing readable explanations, though it is incremental as it builds on existing LLM capabilities.
The paper tackles the challenge of detecting harmful memes by proposing an explainable approach that uses multimodal debate between Large Language Models to generate contradictory rationales, then fine-tunes a small model as a judge for inference. The method achieves much better performance than state-of-the-art methods on three public datasets and demonstrates superior explanation capabilities.
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.