用大模型生成背景信息，提升隐性仇恨言论识别准确率
Leveraging LLMs for Context-Aware Implicit Textual and Multimodal Hate Speech Detection
- 让大模型动态生成语境信息，增强分类器输入
- 文本和多模态任务分别提升3和6个F1点
- 适合研究社交媒体内容安全与大模型应用的读者
本研究提出一种新型隐性仇恨言论检测方法，利用大语言模型（LLMs）作为动态知识库，生成背景上下文并融入分类器输入。比较了两种上下文生成策略：基于命名实体与全文提示；四种融合方式：文本拼接、嵌入拼接、分层Transformer融合及大模型驱动的文本增强。在隐性仇恨文本数据集Latent Hatred上进行实验，并在多模态场景下应用于女性贬损表情包数据集MAMI。结果表明，上下文信息及其融合方式至关重要，相比零上下文基线，文本与多模态设置下的最高性能系统分别提升3和6个F1分数，最佳方案为嵌入拼接。
原文摘要 · Abstract (English)
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.
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