Investigating Annotator Bias in Large Language Models for Hate Speech Detection
It addresses annotator bias in LLMs for hate speech detection, which is crucial for researchers and practitioners using LLMs in data annotation, but is incremental as it builds on existing evaluations of LLM efficacy.
The paper investigated biases in four large language models (GPT-3.5, GPT-4o, Llama-3.1, and Gemma-2) when annotating hate speech data, focusing on gender, race, religion, and disability, and introduced a custom dataset, HateBiasNet, for analysis.
Data annotation, the practice of assigning descriptive labels to raw data, is pivotal in optimizing the performance of machine learning models. However, it is a resource-intensive process susceptible to biases introduced by annotators. The emergence of sophisticated Large Language Models (LLMs) presents a unique opportunity to modernize and streamline this complex procedure. While existing research extensively evaluates the efficacy of LLMs, as annotators, this paper delves into the biases present in LLMs when annotating hate speech data. Our research contributes to understanding biases in four key categories: gender, race, religion, and disability with four LLMs: GPT-3.5, GPT-4o, Llama-3.1 and Gemma-2. Specifically targeting highly vulnerable groups within these categories, we analyze annotator biases. Furthermore, we conduct a comprehensive examination of potential factors contributing to these biases by scrutinizing the annotated data. We introduce our custom hate speech detection dataset, HateBiasNet, to conduct this research. Additionally, we perform the same experiments on the ETHOS (Mollas et al. 2022) dataset also for comparative analysis. This paper serves as a crucial resource, guiding researchers and practitioners in harnessing the potential of LLMs for data annotation, thereby fostering advancements in this critical field.