用于少样本仇恨模因检测的模块化网络
Modularized Networks for Few-shot Hateful Meme Detection
AI总结：
针对少样本仇恨模因检测问题，提出基于LoRA组合性的模块化网络，通过训练模块组合器为各LoRA模块分配权重，在三个数据集上表现优于传统上下文学习且推理开销更低。
摘要：
本文解决了在仅有少量标注样本的低资源场景下检测仇恨模因的挑战。该方法利用了低秩微调（LoRA）的组合性，这是一种广泛使用的参数高效微调技术。首先，在精选的与仇恨模因检测相关的任务上，使用 LoRA 对大语言模型（LLMs）进行微调，从而生成一组 LoRA 模块。这些模块具备仇恨模因检测所需的核心推理能力。随后，利用少量可用的标注样本来训练一个模块组合器，该组合器根据相关性为各个 LoRA 模块分配权重。模型的可学习参数量与 LoRA 模块的数量成正比。这种以 LLMs 为基础并辅以 LoRA 模块增强的模块化网络，在仇恨模因检测中展现出更强的泛化能力。评估在三个专为少样本学习设计的仇恨模因检测数据集上进行。实验表明，所提方法优于传统的上下文学习，而后者在推理阶段的计算开销更大。
英文摘要：
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. We then use the few available annotated samples to train a module composer, which assigns weights to the LoRA modules based on their relevance. The model's learnable parameters are directly proportional to the number of LoRA modules. This modularized network, underpinned by LLMs and augmented with LoRA modules, exhibits enhanced generalization in the context of hateful meme detection. Our evaluation spans three datasets designed for hateful meme detection in a few-shot learning context. The proposed method demonstrates superior performance to traditional in-context learning, which is also more computationally intensive during inference.We then use the few available annotated samples to train a module composer, which assigns weights to the LoRA modules based on their relevance. The model's learnable parameters are directly proportional to the number of LoRA modules. This modularized network, underpinned by LLMs and augmented with LoRA modules, exhibits enhanced generalization in the context of hateful meme detection. Our evaluation spans three datasets designed for hateful meme detection in a few-shot learning context. The proposed method demonstrates superior performance to traditional in-context learning, which is also more computationally intensive during inference.
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