In recent years, language models have revolutionized the field of natural language processing, providing a foundation for a wide range of applications, from virtual assistants to content generation. Despite their incredible achievements, a significant challenge remains: personalization. The ability of a language model to adapt to an individual user's needs, preferences, and communication style is crucial for creating more engaging, responsive, and user-friendly experiences. To address this challenge, we present LaMP (Language Models Personalization), a comprehensive benchmark designed to assess the personalization capabilities of state-of-the-art language models.

LaMP is designed to evaluate language models across various dimensions of personalization. The benchmark features a diverse set of tasks that require models to demonstrate their ability to learn from limited user-specific information, as well as generalize to new, unforeseen contexts. By providing a robust and standardized evaluation platform, LaMP aims to stimulate research and development efforts in the area of personalized language models, ultimately leading to more effective and human-centric AI systems.