Summary of Harnessing Artificial Intelligence to Combat Online Hate: Exploring the Challenges and Opportunities Of Large Language Models in Hate Speech Detection, by Tharindu Kumarage et al.
Harnessing Artificial Intelligence to Combat Online Hate: Exploring the Challenges and Opportunities of Large Language Models in Hate Speech Detection
by Tharindu Kumarage, Amrita Bhattacharjee, Joshua Garland
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have been successfully applied to various tasks beyond natural language processing, such as text classification. Specifically, LLMs can be used for identifying hateful or toxic speech, a challenging and ethically complex task. This study aims to provide a comprehensive review of the current literature on LLMs as classifiers, focusing on their role in detecting and classifying hateful content. Furthermore, the paper empirically evaluates the performance of several LLMs in classifying hate speech, investigating which models excel in this task and what attributes contribute to their proficiency or lack thereof. By combining a thorough literature review with an empirical analysis, this study sheds light on the capabilities and limitations of LLMs in detecting hateful content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and generate human-like text. One cool thing they can do is help identify hate speech online – which is really important because it’s hard to detect and can be very harmful. This study looks at what we currently know about using these models for this task, as well as how different models perform in identifying hate speech. By studying both the existing research and testing different models, this paper helps us understand what makes some language models good at detecting hate speech and what makes others not so good. |
Keywords
» Artificial intelligence » Natural language processing » Text classification
