Summary of Investigating Annotator Bias in Large Language Models For Hate Speech Detection, by Amit Das et al.
Investigating Annotator Bias in Large Language Models for Hate Speech Detection
by Amit Das, Zheng Zhang, Najib Hasan, Souvika Sarkar, Fatemeh Jamshidi, Tathagata Bhattacharya, Mostafa Rahgouy, Nilanjana Raychawdhary, Dongji Feng, Vinija Jain, Aman Chadha, Mary Sandage, Lauramarie Pope, Gerry Dozier, Cheryl Seals
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty Summary: Data annotation is crucial for optimizing machine learning models, but it’s a time-consuming process prone to biases. This paper explores the biases present in Large Language Models (LLMs) when annotating hate speech data, specifically focusing on gender, race, religion, and disability categories with four LLMs: GPT-3.5, GPT-4o, Llama-3.1, and Gemma-2. The study analyzes potential factors contributing to these biases by examining the annotated data and introduces a custom hate speech detection dataset, HateBiasNet, for comparative analysis. This research aims to guide researchers and practitioners in harnessing LLMs’ potential for data annotation, promoting advancements in this critical field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Data labeling is important for making machine learning models work well, but it’s a difficult process that can be biased. This study looks at how Large Language Models (LLMs) do when they’re used to label hate speech data, and what biases are present in their results. The researchers focus on four areas: gender, race, religion, and disability, and use different LLMs to see if there are differences. They also create a special dataset for studying these biases. This study is important because it helps us understand how to make the most of LLMs when we’re labeling data. |
Keywords
» Artificial intelligence » Data labeling » Gpt » Llama » Machine learning
