Modularized Networks for Few-shot Hateful Meme Detection

arXiv:2402.11845v1 Announce Type: cross
Abstract: In this paper, we address the challenge of detecting hateful memes in the low-resource setting where only a few labeled examples are available. Our approach leverages the compositionality of Low-rank adaptation (LoRA), a widely used parameter-efficient tuning technique. We commence by fine-tuning large language models (LLMs) with LoRA on selected tasks pertinent to hateful meme detection, thereby generating a suite of LoRA modules. These modules are capable of essential reasoning skills for hateful meme detection. …

abstract arxiv challenge cs.cl cs.cv detection examples few-shot fine-tuning language language models large language large language models llms lora low low-rank adaptation meme memes networks paper type