HateCOT: A Dataset for Offensive Speech Detection

Core Concepts

Offensive speech detection is enhanced by the HateCOT dataset, improving model performance and providing explanations.

Abstract

The paper introduces the HateCOT dataset for offensive speech detection.
Offensive content detection challenges are discussed, emphasizing the need for reliable models.
HateCOT's impact on pre-training models and fine-tuning is highlighted.
Data curation challenges and the importance of explanations in content moderation are addressed.
Experiments on zero-shot classification, in-domain fine-tuning, and in-context learning are detailed.
Quality assessment of explanations generated by different models is conducted.
Limitations and ethical considerations are acknowledged.

Stats

"We introduce HateCOT, a dataset of 52,000 samples drawn from diverse sources with explanations."
"Our repository is available at https://github.com/hnghiem-usc/hatecot."
"Sophisticated models tend to be data-hungry."

Quotes

"We curate and release HateCOT (Hate-related Chains-of-Thought) a dataset of 52k samples consisting of input text, a hate speech label, and an explanation of that label."
"Our repository is available at https://github.com/hnghiem-usc/hatecot."

Key Insights Distilled From

HateCOT

by Huy ... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11456.pdf

Deeper Inquiries

How can the HateCOT dataset be utilized beyond offensive speech detection?

HateCOT, with its explanations generated by GPT-3.5-Turbo and human curation, can be leveraged in various ways beyond offensive speech detection. One potential application is in improving natural language understanding models by providing diverse and detailed explanations for different types of text data. These explanations can help enhance model interpretability and transparency, leading to more trustworthy AI systems. Additionally, HateCOT could be used in educational settings to teach students about the nuances of offensive language and how it is detected by machine learning models. By analyzing the explanations provided in HateCOT, students can gain insights into the complexities of hate speech classification while also learning about ethical considerations in AI.

What potential biases or limitations could arise from relying heavily on large language models like GPT?

Relying heavily on large language models like GPT may introduce several biases and limitations that need to be carefully addressed. One major concern is bias amplification, where existing biases present in the training data are perpetuated and even magnified by the model's predictions. This can lead to discriminatory outcomes, especially in sensitive tasks such as hate speech detection. Moreover, large language models have been criticized for their lack of contextual understanding and tendency to generate outputs that may not align with societal norms or values.
Another limitation is related to explainability - while these models provide impressive performance on various tasks, they often struggle to provide transparent justifications for their decisions. This opacity can hinder trust in AI systems and make it challenging for users to understand why a particular prediction was made.
Additionally, there are concerns about computational resources required for training and fine-tuning these large models, which may limit accessibility for researchers with limited resources.

How might the concept of explainable AI impact future developments in content moderation?

The concept of explainable AI has significant implications for future developments in content moderation, particularly regarding transparency and accountability. By incorporating explainability features into content moderation systems, platforms can provide users with clear justifications for flagged content or decisions made by automated tools.
Explainable AI can also help improve user trust by offering insights into how moderation decisions are reached based on specific criteria or policies set forth by platform guidelines. Users will have a better understanding of why certain content is removed or labeled as offensive.
Furthermore, explainable AI enables stakeholders such as regulators or auditors to audit decision-making processes within content moderation systems more effectively. It allows them to verify compliance with legal requirements around harmful content removal without compromising user privacy or proprietary algorithms.
Overall,
the integration of explainable AI techniques
in content moderation holds promise
for enhancing transparency,
accountability,
and user trust
in online platforms' moderating practices.
It paves
the way towards more responsible
and effective management
of online discourse,
ensuring a safer digital environment
for all users involved.