Core Concepts
The author explores the efficacy of large language models in detecting hate speech, emphasizing the capabilities and constraints of LLMs in this crucial domain.
Abstract
The content delves into the use of Large Language Models (LLMs) for hate speech detection. It discusses the challenges and opportunities presented by LLMs, focusing on their role as classifiers in identifying hateful or toxic content. The study includes a literature review on LLMs as classifiers and an empirical analysis to evaluate their effectiveness in classifying hate speech. Key points include the performance of different LLMs like GPT-3.5, Llama 2, and Falcon, insights on prompting techniques, error analysis, and best practices for optimizing LLM performance. The study highlights the importance of clear prompts, error analysis to identify model limitations, and strategies to mitigate spurious correlations influencing hate speech classification.
Stats
HateCheck dataset features annotations categorizing hate speech as 'directed' or 'general'
GPT-3.5 and Llama 2 show accuracy levels between 80-90% in classifying hate speech
Error rates vary for different types of hate targets across LLMs
Quotes
"Large language models excel in diverse applications beyond language generation." - Tharindu Kumarage
"Hate speech detection is a challenge due to its subjective nature and context dependency." - Amrita Bhattacharjee
"GPT-3 outperforms other models due to advanced iterations and larger parameter size." - Joshua Garland
"Clear and concise prompts yield superior performance in hate speech classification." - Arizona State University
"Spurious correlations can influence model reliance on specific words or phrases for classification." - Equal Contribution Authors