They can absolutely fail independently. Our main argument is that when several fail together, the effects compound. I like your model-selection point too. That's probably worth making even more explicit.
Thank you for your thoughts!
They can absolutely fail independently. Our main argument is that when several fail together, the effects compound. I like your model-selection point too. That's probably worth making even more explicit.
Thank you for your thoughts!
1. who/what is represented in the data,
2. whose outcomes are visible in evaluation,
3. who can identify and contest failures after deployment, and
1. who/what is represented in the data,
2. whose outcomes are visible in evaluation,
3. who can identify and contest failures after deployment, and
* Findings highlight the potential for advanced LLMs to improve content moderation systems across platforms.
* Findings highlight the potential for advanced LLMs to improve content moderation systems across platforms.
* LLM-based approaches show high accuracy and low false positives/negatives in detecting toxic content.
* We test robustness to adversarial attacks (input perturbations, prompt injections) and observe varying resilience across models.
* LLM-based approaches show high accuracy and low false positives/negatives in detecting toxic content.
* We test robustness to adversarial attacks (input perturbations, prompt injections) and observe varying resilience across models.
We benchmark both existing moderation tools (OpenAI moderation model, Llama-Guard-3) and leading LLMs (GPT, Gemini, Claude, Gemma) under normal and adversarial conditions.
We benchmark both existing moderation tools (OpenAI moderation model, Llama-Guard-3) and leading LLMs (GPT, Gemini, Claude, Gemma) under normal and adversarial conditions.
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