让大模型主动生成界面，提升多轮交互效率。
Generative Interfaces for Language Models
- 大模型根据用户需求自动生成可操作界面
- 人机交互偏好提升最高达72%
- 适合需要探索性任务的智能助手设计
大型语言模型（LLMs）正被广泛视为助手、协作者和顾问，可通过自然对话支持多种任务。然而，现有系统多受限于线性的请求-响应模式，在多轮、信息密集和探索性任务中常导致交互低效。为此，我们提出生成式界面（Generative Interfaces for LLMs），即让大模型通过主动生成用户界面（UI）来实现更灵活、互动更强的参与方式。该框架利用特定于界面的结构化表示与迭代优化，将用户查询转化为任务定制的界面。为系统评估，我们构建了多维度评估体系，从不同任务、交互模式和查询类型对比生成式界面与传统对话方式在功能、交互性和情感体验上的表现。结果表明，生成式界面在各项指标上均显著优于传统对话，人类偏好度最高提升72%。研究揭示了用户偏好生成式界面的条件与原因，为未来人机交互发展提供方向。数据与代码已开源：https://github.com/SALT-NLP/GenUI。
原文摘要 · Abstract (English)
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, information-dense, 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 task-specific 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.
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