Personalizing LLM Responses to Combat Political Misinformation

著者: Adiba Mahbub Proma, Neeley Pate, James Druckman, Gourab Ghoshal, Ehsan Hoque - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization 2025 被引用: 5

Despite various efforts to tackle online misinformation, people inevitably encounter and engage with it, especially on social media platforms.Recent advances in LLMs present an opportunity to develop personalized interventions to address misinformed beliefs, and potentially offer more effective approaches than existing nontailored methods.In this paper, we design and evaluate personalized LLM agent that can consider users' demographics and personalities to tailor responses to mitigate misinformed beliefs.Our pipeline is grounded in facts through an external Retrieval Augmented Generation (RAG) knowledge base and is able to generate diverse output as a result of the personalization, with an average cosine similarity of 0.538.Our pipeline scores an average rating of 3.99 out of 5 when evaluated by a GPT-4o-mini LLM judge for response persuasiveness.Our methods can be adapted to design similar personalized agents in other domains.