Towards Efficient and Explainable Hate Speech Detection via Model Distillation

• URL: http://arxiv.org/abs/2412.13698v1
• Date: Wed, 18 Dec 2024 10:42:53 GMT
• Title: Towards Efficient and Explainable Hate Speech Detection via Model Distillation
• Authors: Paloma Piot, Javier Parapar,
• Abstract summary: Large Language Models (LLMs) have proven effective for hate speech detection and to promote interpretability. We propose distilling big language models by using Chain-of-Thought to extract explanations that support the hate speech classification task. We demonstrate that distilled models deliver explanations of the same quality as larger models while surpassing them in classification performance.
• Score: 2.433983268807517
• License:
• Abstract: Automatic detection of hate and abusive language is essential to combat its online spread. Moreover, recognising and explaining hate speech serves to educate people about its negative effects. However, most current detection models operate as black boxes, lacking interpretability and explainability. In this context, Large Language Models (LLMs) have proven effective for hate speech detection and to promote interpretability. Nevertheless, they are computationally costly to run. In this work, we propose distilling big language models by using Chain-of-Thought to extract explanations that support the hate speech classification task. Having small language models for these tasks will contribute to their use in operational settings. In this paper, we demonstrate that distilled models deliver explanations of the same quality as larger models while surpassing them in classification performance. This dual capability, classifying and explaining, advances hate speech detection making it more affordable, understandable and actionable.

Related papers

• OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models [55.63479003621053]
  We introduce OWLS, an open-access suite of multilingual speech recognition and translation models. We use OWLS to derive neural scaling laws, showing how final performance can be reliably predicted when scaling. We show how OWLS can be used to power new research directions by discovering emergent abilities in large-scale speech models.
  arXiv  Detail & Related papers  (2025-02-14T18:51:40Z)
• Dealing with Annotator Disagreement in Hate Speech Classification [0.0]
  This paper examines strategies for addressing annotator disagreement, an issue that has been largely overlooked. We evaluate different approaches to deal with annotator disagreement regarding hate speech classification in Turkish tweets, based on a fine-tuned BERT model. Our work highlights the importance of the problem and provides state-of-art benchmark results for detection and understanding of hate speech in online discourse.
  arXiv  Detail & Related papers  (2025-02-12T10:19:50Z)
• Hierarchical Sentiment Analysis Framework for Hate Speech Detection: Implementing Binary and Multiclass Classification Strategy [0.0]
  We propose a new multitask model integrated with shared emotional representations to detect hate speech across the English language. We conclude that utilizing sentiment analysis and a Transformer-based trained model considerably improves hate speech detection across multiple datasets.
  arXiv  Detail & Related papers  (2024-11-03T04:11:33Z)
• Towards Interpretable Hate Speech Detection using Large Language Model-extracted Rationales [15.458557611029518]
  Social media platforms are a prominent arena for users to engage in interpersonal discussions and express opinions. There arises a need to automatically identify and flag instances of hate speech. We propose to use state-of-the-art Large Language Models (LLMs) to extract features in the form of rationales from the input text.
  arXiv  Detail & Related papers  (2024-03-19T03:22:35Z)
• An Investigation of Large Language Models for Real-World Hate Speech Detection [46.15140831710683]
  A major limitation of existing methods is that hate speech detection is a highly contextual problem. Recently, large language models (LLMs) have demonstrated state-of-the-art performance in several natural language tasks. Our study reveals that a meticulously crafted reasoning prompt can effectively capture the context of hate speech.
  arXiv  Detail & Related papers  (2024-01-07T00:39:33Z)
• HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning [29.519687405350304]
  We introduce a hate speech detection framework, HARE, which harnesses the reasoning capabilities of large language models (LLMs) to fill gaps in explanations of hate speech. Experiments on SBIC and Implicit Hate benchmarks show that our method, using model-generated data, consistently outperforms baselines. Our method enhances the explanation quality of trained models and improves generalization to unseen datasets.
  arXiv  Detail & Related papers  (2023-11-01T06:09:54Z)
• Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning [56.03057119008865]
  We show that scaling diffusion language models can effectively make them strong language learners. We build competent diffusion language models at scale by first acquiring knowledge from massive data. Experiments show that scaling diffusion language models consistently improves performance across downstream language tasks.
  arXiv  Detail & Related papers  (2023-08-23T16:01:12Z)
• Leveraging World Knowledge in Implicit Hate Speech Detection [5.5536024561229205]
  We show that real world knowledge about entity mentions in a text does help models better detect hate speech. We also discuss cases where real world knowledge does not add value to hate speech detection.
  arXiv  Detail & Related papers  (2022-12-28T21:23:55Z)
• Shapley Head Pruning: Identifying and Removing Interference in Multilingual Transformers [54.4919139401528]
  We show that it is possible to reduce interference by identifying and pruning language-specific parameters. We show that removing identified attention heads from a fixed model improves performance for a target language on both sentence classification and structural prediction.
  arXiv  Detail & Related papers  (2022-10-11T18:11:37Z)
• Deep Learning for Hate Speech Detection: A Comparative Study [54.42226495344908]
  We present here a large-scale empirical comparison of deep and shallow hate-speech detection methods. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. 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.
  arXiv  Detail & Related papers  (2022-02-19T03:48:20Z)
• Addressing the Challenges of Cross-Lingual Hate Speech Detection [115.1352779982269]
  In this paper we focus on cross-lingual transfer learning to support hate speech detection in low-resource languages. We leverage cross-lingual word embeddings to train our neural network systems on the source language and apply it to the target language. We investigate the issue of label imbalance of hate speech datasets, since the high ratio of non-hate examples compared to hate examples often leads to low model performance.
  arXiv  Detail & Related papers  (2022-01-15T20:48:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.