集成大语言模型的自动仇恨言论识别方法：使用可控文本生成模型
LLMs-Integrated Automatic Hate Speech Recognition Using Controllable Text Generation Models
AI总结：
本文提出利用大语言模型和可控文本生成模型，通过课程学习逐步训练以提高仇恨言论识别的准确率和效率。
摘要：
本文提出了一种利用大语言模型（LLMs）的自动语音识别（ASR）模型，用于识别仇恨言论。所提出的方法将ASR模型的编码器与LLMs的解码器集成，使同时进行转录和审查任务成为可能，以防止有害内容的暴露。通过指令微调LLM来用特定标记掩蔽与仇恨相关的词汇，需要一个标注的仇恨言论数据集，这受到限制。我们使用带有链式思维（CoT）提示技术的LLM生成文本样本，该技术受文化背景和示例的指导，然后将其转换为语音样本使用文本到语音（TTS）系统。然而，其中一些样本包含非仇恨言论但含有仇恨相关词汇的样本，这会降低审查性能。本文过滤那些文本分类模型正确标记为仇恨内容的样本。通过调整正确回答模型数量的阈值，我们可以控制生成数据集中仇恨的程度，使我们能够通过课程学习逐步训练LLMs。实验结果表明，所提出的方法在掩蔽仇恨相关词汇的准确率达到了58.6%，超过了之前的基线。我们还确认课程训练有助于提高转录和审查任务的效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
