Eye-Opening AI Analysis
HATEBENCH: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate Campaigns
This paper introduces HATEBENCH, a framework for benchmarking hate speech detectors against LLM-generated hate speech and hate campaigns. It reveals that existing detectors, while generally effective, lose performance on newer LLMs like GPT-4. Furthermore, LLM-driven hate campaigns can easily evade detection through adversarial attacks and model stealing, increasing efficiency by 13-21x. The study highlights the urgent need for continuous updates and more robust defenses against these emerging threats.
Key Research Findings at a Glance
Highlighting the most impactful metrics for enterprise decision-makers, directly from the research.
0.878
F1-score (GPT-3.5)
0.621
F1-score (GPT-4)
0.966
Attack Success Rate
13x
Efficiency Increase
Deep Analysis & Enterprise Applications
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0.621
F1-score on GPT-4 (Perspective Detector)
| Detector | GPT-3.5 F1 | GPT-4 F1 |
|---|---|---|
| Perspective | 0.878 | 0.621 |
| Moderation | 0.905 | 0.658 |
| TweetHate | 0.840 | 0.824 |
Enterprise Process Flow
LLMs generate hate speech
→
Adversary modifies content
→
Hate Speech Detector bypassed
→
Hate Campaign launched
Adversarial Attack Evasion
The study demonstrates that state-of-the-art hate speech detectors are vulnerable to adversarial attacks. The most potent attack, TextFooler, achieved an Attack Success Rate (ASR) of 0.966 on Perspective, Moderation, and TweetHate. This means nearly all adversarially crafted hate speech could bypass detection, posing a significant threat to online platforms.
The ability to easily evade detection opens the door for automated, large-scale hate campaigns, leveraging LLMs to generate and distribute malicious content without human oversight. This necessitates the development of more robust and adaptable detection mechanisms.
13x
Efficiency Increase via Model Stealing
| Surrogate Model | Target Detector | Agreement | Accuracy |
|---|---|---|---|
| RoBERTa | Perspective | 0.955 | 0.841 |
| BERT | Moderation | 0.933 | 0.858 |
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