Diverse User

Research on diverse user modeling focuses on adapting large language models (LLMs) to effectively serve users with varied preferences and behaviors. Current efforts concentrate on developing methods for personalized preference optimization, often employing techniques like multi-objective reward modeling and base-anchored preference optimization to mitigate knowledge loss during personalization. This work is crucial for improving the fairness, inclusivity, and overall effectiveness of LLMs in real-world applications, such as personalized recommendations and fake news detection, where understanding diverse user engagement patterns is paramount. The development of robust benchmarks and datasets for evaluating pluralistic alignment is also a key area of focus.