用于大语言模型伦理调优的韩语有毒指令数据集的自动构建
Automatic Construction of a Korean Toxic Instruction Dataset for Ethical Tuning of Large Language Models
AI总结：
本文针对大语言模型伦理调优中人工标注成本高且有毒数据稀缺的问题，提出了自动构建的韩语有毒指令数据集KoTox，包含39K条不道德指令-输出对，用于提升模型的伦理意识与对有毒输入的响应能力。
摘要：
注意：本文可能包含令人反感或不适的内容。大语言模型（LLMs）的出现迫切需要开发能够减少不道德语言生成并妥善处理有毒用户查询的训练方法。鉴于人工劳动相关的挑战和数据稀缺问题，我们提出了KoTox，包含39K条不道德指令-输出对。该自动生成的有毒指令集合优化了LLMs的训练，并为提升LLMs的伦理意识及对各种有毒输入的响应能力建立了基础框架，从而在自然语言处理（NLP）应用中促进更安全、更负责任的交互。
英文摘要：
Caution: this paper may include material that could be offensive or distressing. The advent of Large Language Models (LLMs) necessitates the development of training approaches that mitigate the generation of unethical language and aptly manage toxic user queries. Given the challenges related to human labor and the scarcity of data, we present KoTox, comprising 39K unethical instruction-output pairs. This collection of automatically generated toxic instructions refines the training of LLMs and establishes a foundational framework for improving LLMs' ethical awareness and response to various toxic inputs, promoting more secure and responsible interactions in Natural Language Processing (NLP) applications.
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。
