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arXiv AI By Akash Bonagiri, Lucen Li, Rajvardhan Oak, Zeerak Babar, Magdalena Wojcieszak, Anshuman Chhabra

Towards Safer Social Media Platforms: Scalable and Performant Few-Shot Harmful Content Moderation Using Large Language Models

Read the original on arXiv AI →

The paper presents a scalable approach to harmful content moderation on social media by leveraging large language models (LLMs) for few-shot, in-context learning. Experiments across multiple LLMs show that this method outperforms proprietary baselines such as Perspective and OpenAI Moderation, as well as prior few-shot learning techniques, in detecting harmful content. The study also explores the addition of visual cues like video thumbnails to assess multimodal improvements, highlighting the advantages of LLM-based moderation for dynamic and large-scale content filtering.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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