Generative Interfaces for Language Models

Large language models (LLMs) are increasingly seen as assistants, copilots, and consultants, capable of supporting a wide range of tasks through natural conversation.However, most systems remain constrained by a linear request-response format that often makes interactions inefficient in multi-turn, informationdense, and exploratory tasks.To address these limitations, we propose Generative Interfaces for Language Models, a paradigm in which LLMs respond to user queries by proactively generating user interfaces (UIs) that enable more adaptive and interactive engagement.Our framework leverages structured interface-specific representations and iterative refinements to translate user queries into taskspecific UIs.For systematic evaluation, we introduce a multidimensional assessment framework that compares generative interfaces with traditional chat-based ones across diverse tasks, interaction patterns, and query types, capturing functional, interactive, and emotional aspects of user experience.Results show that generative interfaces consistently outperform conversational ones, with up to a 72% improvement in human preference.These findings clarify when and why users favor generative interfaces, paving the way for future advancements in human-AI interaction.Data and code are available at https://github.com/ SALT-NLP/GenUI. InstructionA longstanding goal in computing is to design systems that not only respond to users but also adapt by dynamically reshaping interfaces to facilitate users' interaction and help them achieve their goals (Apple Inc., 1987;Lyytinen and Yoo, 2002).While recent advances in large language models * First two authors contributed equally.