# Automatic Construction of a Korean Toxic Instruction Dataset for Ethical Tuning of Large Language Models

arXiv: https://arxiv.org/abs/2311.18215 · HTML: https://mlsanity.com/paper/2311.18215/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://mlsanity.com/paper/2111.07997/) (2021)
- [A New Generation of Perspective API: Efficient Multilingual Character-level Transformers](https://mlsanity.com/paper/2202.11176/) (2022)
- [Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack](https://mlsanity.com/paper/1908.06083/) (2019)
- [Recipes for Safety in Open-domain Chatbots](https://mlsanity.com/paper/2010.07079/) (2020)
- [Directions in Abusive Language Training Data: Garbage In, Garbage Out](https://mlsanity.com/paper/2004.01670/) (2020)
- [Explore, Establish, Exploit: Red Teaming Language Models from Scratch](https://mlsanity.com/paper/2306.09442/) (2023)
- [Challenges in Automated Debiasing for Toxic Language Detection](https://mlsanity.com/paper/2102.00086/) (2021)
- [What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers](https://mlsanity.com/paper/2109.04650/) (2021)
- [BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection](https://mlsanity.com/paper/2005.12503/) (2020)
- [KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application](https://mlsanity.com/paper/2305.17701/) (2023)
- [A Technical Report for Polyglot-Ko: Open-Source Large-Scale Korean Language Models](https://mlsanity.com/paper/2306.02254/) (2023)
- [Data Expansion using Back Translation and Paraphrasing for Hate Speech Detection](https://mlsanity.com/paper/2106.04681/) (2021)
- [Reward Modeling for Mitigating Toxicity in Transformer-based Language Models](https://mlsanity.com/paper/2202.09662/) (2022)
- [Adding Instructions during Pretraining: Effective Way of Controlling Toxicity in Language Models](https://mlsanity.com/paper/2302.07388/) (2023)
- [An Invariant Learning Characterization of Controlled Text Generation](https://mlsanity.com/paper/2306.00198/) (2023)
- [APEACH: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets](https://mlsanity.com/paper/2202.12459/) (2022)

## Built on by (most cited first, 1 of 1)

- [Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM](https://mlsanity.com/paper/2508.05775/) (2025)

## Abstract (arXiv)

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.