HARE : Explainable Hate Speech Detection with Step-by-Step Reasoning (2023)

Abstract: With the proliferation of social media, accurate detection of hate speech has become critical to ensure safety online. To combat nuanced forms of hate speech, it is important to identify and thoroughly explain hate speech to help users understand its harmful effects. Recent benchmarks have attempted to tackle this issue by training generative models on free-text annotations of implications in hateful text. However, we find significant reasoning gaps in the existing annotations schemes, which may hinder the supervision of detection models. In this paper, we introduce a hate speech detection framework, **HARE**, which harnesses the reasoning capabilities of large language models (LLMs) to fill these gaps in explanations of hate speech, thus enabling effective supervision of detection models. Experiments on SBIC and Implicit Hate benchmarks show that our method, using model-generated data, consistently outperforms baselines, using existing free-text human annotations. Analysis demonstrates that our method enhances the explanation quality of trained models and improves generalization to unseen datasets. Our code is available at https://github.com/joonkeekim/hare-hate-speech.git.

## introduction the increase in the use of online media has intensified the exposure to hate speech, prompting the need for effective detection systems @xcite . while early works have been limited to the classification of explicit hate speech @xcite , recent works have drawn our attention to implicit forms of hate speech which are more prevalent, yet subtle. @xcite . to tackle these nuanced forms of hate speech, it is important for systems to not only identify hate speech but also provide interpretable explanations * equal contribution † corresponding authors (a) recent benchmarks on understanding hate speech provide free-text annotations on the implications of hate speech, but gaps in reasoning hinder the supervision of generative detection models. (b) we propose the use of llms to fill in the gaps and enable detection models to understand and explain hate speech. @xcite . this can help mitigate distributional biases inherent in simple classification, allowing people to understand and reason about the potential harms of hateful text @xcite . explanations can also improve the transparency of content moderation on social media @xcite . recent works on hate speech understanding @xcite @xcite have considered training autoregressive language models to generate underlying explanations on hate speech. the models are trained on humanwritten free-text rationales such as implied statements and targeted groups. however, despite the use of novel benchmark datasets, i.e., sbic @xcite and implicit hate @xcite , the trained models struggle to generate detailed and comprehensive explanations. moreover, we observe that the provided rationales give marginal improvement to detection performance under joint training. a potential cause of the limited supervision provided by existing annotations on understanding and explaining hate speech may be the existence of critical gaps in reasoning. for example, as shown in figure 1 , the implied statement of the post "how dark is my humour? it picks cotton" is annotated as "black folks are slaves", in sbic. to understand this implication, one must understand that "dark" implies "black folks", and the phrase "picks cotton" relates to the historical background of african americans. while this may be obvious to human annotators, language models are known to lack societal knowledge and commonsense reasoning skills to understand these nuances @xcite @xcite . this leaves a significant gap between the training objectives of classification and generating annotated implications, which may harm supervision @xcite . drawing inspiration from the reasoning capabilities of large language models (llms) improved with chain-of-thought (cot) reasoning @xcite , we present our novel approach "explainable hate speech detection with @xcite reasoning (hare)". we leverage llm-generated free-text rationales using cot prompts to fill in the gaps of reasoning in existing hate speech annotations and enhance supervision of generative detection models. to create these rationales, we propose two approaches: (1) adopt cot prompts to create comprehensive rationales that align with the given texts and (2) incorporate existing human annotations from benchmarks in the cot prompts to bridge the logical gap between the input text and human annotations. when tested on the challenging sbic and implicit hate datasets, our approach outperforms standard fine-tuning with given human annotations and provides enhanced explanations behind the detection results. ## conclusion in this paper, we present hare framework to improve the ability of the language model to understand hate speech and provide clearer explanations for its decisions. we propose utilizing cot reasonings extracted from llms in two variants to overcome the logical gaps in human-annotated rationales. when fine-tuned on the sbic and implicit hate datasets, our methods achieve superior detection performance and better qualified explanations.