Proactive AI writing assistants need to predict when users want drafting help, yet we lack empirical understanding of what drives preferences. Through a factorial vignette study with 50 participants making 750 pairwise comparisons, we find compositional effort dominates decisions ($ρ= 0.597$) while urgency shows no predictive power ($ρ\approx 0$). More critically, users exhibit a striking perception-behavior gap: they rank urgency first in self-reports despite it being the weakest behavioral driver, representing a complete preference inversion. This misalignment has measurable consequences. Systems designed from users' stated preferences achieve only 57.7\% accuracy, underperforming even naive baselines, while systems using behavioral patterns reach significantly higher 61.3\% ($p < 0.05$). These findings demonstrate that relying on user introspection for system design actively misleads optimization, with direct implications for proactive natural language generation (NLG) systems.
MODOC: A Modular Interface for Flexible Interlinking of Text Retrieval and Text Generation Functions
Institute of Neuroinformatics, University of Zurich and ETH Zurich（苏黎世大学与苏黎世联邦理工学院神经信息学研究所）；ETH Zurich（苏黎世联邦理工学院）；Linguistic Research Infrastructure, University of Zurich（苏黎世大学语言研究基础设施）
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Center for Data Science, Peking University（北京大学数据科学中心）；Microsoft（微软）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；State Key Laboratory of Media Convergence Production Technology and Systems（媒体融合生产技术与系统国家重点实验室）
本文提出Smart Word Suggestions（SWS）任务与基准，通过端到端评估识别写作中需改进的词汇并提供替换建议，构建了含人工标注测试集与远监督训练集的评估框架，实验表明该任务具挑战性并指明未来方向。
Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters
Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）；Alibaba Group（阿里巴巴集团）；Baidu Inc.（百度公司）；OPPO Research Institute（OPPO研究院）；Sun-Yat Sen University（中山大学）
