Abstract
This research introduces a novel approach to textual and multimodal Hate Speech Detection (HSD), using Large Language Models (LLMs) as dynamic knowledge bases to generate background context and incorporate it into the input of HSD classifiers. Two context generation strategies are examined: one focused on named entities and the other on full-text prompting. Four methods of incorporating context into the classifier input are compared: text concatenation, embedding concatenation, a hierarchical transformer-based fusion, and LLM-driven text enhancement. Experiments are conducted on the textual Latent Hatred dataset of implicit hate speech and applied in a multimodal setting on the MAMI dataset of misogynous memes. Results suggest that both the contextual information and the method by which it is incorporated are key, with gains of up to 3 and 6 F1 points on textual and multimodal setups respectively, from a zero-context baseline to the highest-performing system, based on embedding concatenation.
Abstract (translated)
这项研究提出了一种新颖的方法来检测文本和多模态仇恨言论（HSD），该方法利用大型语言模型（LLMs）作为动态知识库，生成背景信息并将其整合到HSD分类器的输入中。研究考察了两种上下文生成策略：一种侧重于命名实体，另一种侧重于全文提示。比较了四种将上下文融入分类器输入的方法：文本拼接、嵌入拼接、基于分层变换器融合和由LLM驱动的文本增强方法。实验在包含隐含仇恨言论的Latent Hatred数据集上进行，并在包含厌女表情包的MAMI多模态数据集中应用。结果表明，上下文信息及其整合方式至关重要，在从零背景到最高性能系统的基准上，文本和多模态设置分别提高了高达3分和6分的F1分数（基于嵌入拼接）。
URL
https://arxiv.org/abs/2510.15685
