As language models (LMs) become increasingly prevalent in our daily lives, it’s essential to address their potential downfalls, particularly toxicity. This article aims to provide a comprehensive guide on how LMs can be used ethically and responsibly by understanding the nuances of toxicity. We will delve into the various aspects of toxicity, including its definition, types, and limitations, and explore ways to mitigate it.
What is Toxicity in Language Models?
Toxicity refers to the harmful or offensive content generated by LMs, which can have severe consequences, such as promoting hate speech or discrimination. It’s crucial to identify and classify toxic content accurately to develop effective strategies for mitigation. Our study focuses on two types of toxicity: explicit and implicit.
Explicit Toxicity
Explicit toxicity involves the use of offensive language or hate speech, which is easily identifiable and can be addressed through immediate actions, such as removing the content or banning the user.
Implicit Toxicity
Implicit toxicity is more challenging to detect, as it involves subtle biases and stereotypes that can be ingrained in language models’ decision-making processes. These biases can lead to discriminatory language patterns that are not overtly offensive but still harmful.
Limitations of Toxicity Analysis
While toxicity analysis is crucial for ensuring ethical LM use, it faces several limitations:
- Scarcity of Comprehensive Metrics and Benchmark Datasets: Evaluating ethical considerations in LMs is challenging due to the lack of comprehensive metrics and benchmark datasets, especially for Korean LLMs. This hinders the accuracy of toxicity assessments.
- Human Annotator Biases: Rigorous evaluation of sentences by linguistic annotators can be impacted by human biases, which may affect the quality of the auto-generated sentences.
- Limited Scope of Social Media Texts: Most approaches for crawling social media texts restrict the scope to specific domains, limiting generalization.
Our Method: Automatic Sentence Generation and Category Labeling:
To address these limitations, we propose an automatic sentence generation and category labeling method that improves the quality of toxicity assessments. Our approach ensures reliability by rigorously evaluating sentences for naturalness and coherence through a human-in-the-loop evaluation process.
Conclusion
In conclusion, understanding toxicity in LMs is crucial for ethical use. By identifying and classifying toxic content accurately, we can develop effective strategies for mitigation. Our study provides a comprehensive guide on how to address toxicity in LMs, including the definition of toxicity, its types, limitations, and proposed methods for automatic sentence generation and category labeling. By following these guidelines, we can ensure responsible and ethical use of LMs that promote inclusivity, respect, and social responsibility.
