# LLMs-Integrated Automatic Hate Speech Recognition Using Controllable Text Generation Models

arXiv: https://arxiv.org/abs/2601.04654 · HTML: https://mlsanity.com/paper/2601.04654/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 14 of 14)

- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [SALMONN: Towards Generic Hearing Abilities for Large Language Models](https://mlsanity.com/paper/2310.13289/) (2023)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://mlsanity.com/paper/2012.15761/) (2020)
- [De-identification of Patient Notes with Recurrent Neural Networks](https://mlsanity.com/paper/1606.03475/) (2016)
- [A Large Self-Annotated Corpus for Sarcasm](https://mlsanity.com/paper/1704.05579/) (2017)
- [Deep Learning Models for Multilingual Hate Speech Detection](https://mlsanity.com/paper/2004.06465/) (2020)
- [A systematic review of Hate Speech automatic detection using Natural Language Processing](https://mlsanity.com/paper/2106.00742/) (2021)
- [An Investigation of Large Language Models for Real-World Hate Speech Detection](https://mlsanity.com/paper/2401.03346/) (2024)
- [Assessing the impact of contextual information in hate speech detection](https://mlsanity.com/paper/2210.00465/) (2022)
- [Boosting Code-Switching ASR with Mixture of Experts Enhanced Speech-Conditioned LLM](https://mlsanity.com/paper/2409.15905/) (2024)
- [HaT5: Hate Language Identification using Text-to-Text Transfer Transformer](https://mlsanity.com/paper/2202.05690/) (2022)
- [Bridging the Fairness Gap: Enhancing Pre-trained Models with LLM-Generated Sentences](https://mlsanity.com/paper/2501.06795/) (2025)
- [An Investigation Into Explainable Audio Hate Speech Detection](https://mlsanity.com/paper/2408.06065/) (2024)

## Abstract (arXiv)

This paper proposes an automatic speech recognition (ASR) model for hate speech using large language models (LLMs). The proposed method integrates the encoder of the ASR model with the decoder of the LLMs, enabling simultaneous transcription and censorship tasks to prevent the exposure of harmful content. Instruction tuning of the LLM to mask hate-related words with specific tokens requires an annotated hate speech dataset, which is limited. We generate text samples using an LLM with the Chain-of-Thought (CoT) prompting technique guided by cultural context and examples and then convert them into speech samples using a text-to-speech (TTS) system. However, some of them contain non-hate speech samples with hate-related words, which degrades the censorship performance. This paper filters the samples which text classification models correctly label as hate content. By adjusting the threshold for the number of correct answer models, we can control the level of hate in the generated dataset, allowing us to train the LLMs through curriculum learning in a gradual manner. Experimental results show that the proposed method achieves a masking accuracy of 58.6% for hate-related words, surpassing previous baselines. We also confirm that the curriculum training contributes to the efficiency of both transcription and censorship tasks.