Hate speech encompasses verbal, written, or behavioral communication that
targets derogatory or discriminatory language against individuals or groups
based on sensitive characteristics. Automated hate speech detection plays a
crucial role in curbing its propagation, especially across social media
platforms. Various methods, including recent advancements in deep learning,
have been devised to address this challenge. In this study, we introduce
HateTinyLLM, a novel framework based on fine-tuned decoder-only tiny large
language models (tinyLLMs) for efficient hate speech detection. Our
experimental findings demonstrate that the fine-tuned HateTinyLLM outperforms
the pretrained mixtral-7b model by a significant margin. We explored various
tiny LLMs, including PY007/TinyLlama-1.1B-step-50K-105b, Microsoft/phi-2, and
facebook/opt-1.3b, and fine-tuned them using LoRA and adapter methods. Our
observations indicate that all LoRA-based fine-tuned models achieved over 80\%
accuracy.

本研究介绍了一种名为 HateTinyLLM 的基于精调的解码器型微型大型语言模型（tinyLLM）的新框架，用于高效检测仇恨言论。实验证明，精调的 HateTinyLLM 在检测效果上明显优于预训练的 mixtral-7b 模型。我们尝试了多个微型 LLM，包括 PY007/TinyLlama-1.1B-step-50K-105b、Microsoft/phi-2 和 facebook/opt-1.3b，并通过 LoRA 和适配器方法进行了精调。观察结果表明，所有基于 LoRA 的精调模型的准确率都超过 80%。

HateTinyLLM：使用微型大型语言模型进行仇恨言论检测

HateTinyLLM : Hate Speech Detection Using Tiny Large Language Models

Automated hate speech detection is an important tool in combating the spread
of hate speech, particularly in social media. Numerous methods have been
developed for the task, including a recent proliferation of deep-learning based
approaches. A variety of datasets have also been developed, exemplifying
various manifestations of the hate-speech detection problem. We present here a
large-scale empirical comparison of deep and shallow hate-speech detection
methods, mediated through the three most commonly used datasets. Our goal is to
illuminate progress in the area, and identify strengths and weaknesses in the
current state-of-the-art. We particularly focus our analysis on measures of
practical performance, including detection accuracy, computational efficiency,
capability in using pre-trained models, and domain generalization. In doing so
we aim to provide guidance as to the use of hate-speech detection in practice,
quantify the state-of-the-art, and identify future research directions. Code
and dataset are available at
this https URL.

本研究对深度和浅层仇恨言论检测方法进行了大规模实证比较，并以常用数据集为媒介，特别关注实际性能和实践指导，以提供指导，并确定未来的研究方向。

深度学习用于仇恨言论检测：一项比较研究

Deep Learning for Hate Speech Detection: A Comparative Study

Existing work on automated hate speech detection typically focuses on binary
classification or on differentiating among a small set of categories. In this
paper, we propose a novel method on a fine-grained hate speech classification
task, which focuses on differentiating among 40 hate groups of 13 different
hate group categories. We first explore the Conditional Variational Autoencoder
(CVAE) as a discriminative model and then extend it to a hierarchical
architecture to utilize the additional hate category information for more
accurate prediction. Experimentally, we show that incorporating the hate
category information for training can significantly improve the classification
performance and our proposed model outperforms commonly-used discriminative
models.

本文提出了一种新的细粒度仇恨言论分类任务的方法，使用条件变分自编码器作为判别模型，并扩展到分层架构，利用附加的仇恨类别信息进行更准确的预测，实验证明采用该方法显著提高了分类性能且优于常用的判别式模型。