Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms
This addresses the challenge of scalable and adaptable content moderation for social media platforms, though it is incremental as it builds on existing LLM capabilities.
The paper tackles the problem of harmful content exposure on social media by proposing a re-ranking approach using Large Language Models in zero-shot and few-shot settings, demonstrating that it significantly outperforms existing proprietary moderation methods in experiments across three datasets and models.
Social media platforms utilize Machine Learning (ML) and Artificial Intelligence (AI) powered recommendation algorithms to maximize user engagement, which can result in inadvertent exposure to harmful content. Current moderation efforts, reliant on classifiers trained with extensive human-annotated data, struggle with scalability and adapting to new forms of harm. To address these challenges, we propose a novel re-ranking approach using Large Language Models (LLMs) in zero-shot and few-shot settings. Our method dynamically assesses and re-ranks content sequences, effectively mitigating harmful content exposure without requiring extensive labeled data. Alongside traditional ranking metrics, we also introduce two new metrics to evaluate the effectiveness of re-ranking in reducing exposure to harmful content. Through experiments on three datasets, three models and across three configurations, we demonstrate that our LLM-based approach significantly outperforms existing proprietary moderation approaches, offering a scalable and adaptable solution for harm mitigation.