Summary of Advancing Content Moderation: Evaluating Large Language Models For Detecting Sensitive Content Across Text, Images, and Videos, by Nouar Aldahoul et al.
Advancing Content Moderation: Evaluating Large Language Models for Detecting Sensitive Content Across Text, Images, and Videos
by Nouar AlDahoul, Myles Joshua Toledo Tan, Harishwar Reddy Kasireddy, Yasir Zaki
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper addresses the pressing issue of hate speech, harassment, and harmful content spread across various online platforms. It highlights the need for effective technologies to detect and censor such content. The authors discuss how techniques from natural language processing (NLP) and computer vision have been used to automatically identify sensitive content, enabling platforms to enforce content policies at scale. However, existing methods still have limitations in achieving high detection accuracy with fewer false positives and false negatives. To address this challenge, the paper evaluates LLM-based content moderation solutions, including OpenAI’s moderation model and Llama-Guard3, and studies their capabilities to detect sensitive contents. The authors also explore recent LLMs like GPT, Gemini, and Llama in identifying inappropriate contents across media outlets. Various textual and visual datasets are utilized for evaluation and comparison. The results demonstrate that LLMs outperform traditional techniques by achieving higher accuracy and lower false positive and false negative rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to stop bad things from being shared online, like hate speech or violence. It talks about how some algorithms can help identify and remove this kind of content, but these methods aren’t perfect yet. The authors look at different types of artificial intelligence models that can be used to detect harmful content and compare their performance. They use lots of data, including social media posts and videos, to test the effectiveness of these models. Overall, the paper shows that some AI models are better than others at detecting bad content and removing it from online platforms. |
Keywords
» Artificial intelligence » Gemini » Gpt » Llama » Natural language processing » Nlp
