Generative artificial intelligence (GenAI) has ushered in a new era for
storytellers, providing a powerful tool to ignite creativity and explore
uncharted narrative territories. As technology continues to advance, the
synergy between human creativity and AI-generated content holds the potential
to redefine the landscape of storytelling. In this work, we propose SARD, a
drag-and-drop visual interface for generating a multi-chapter story using large
language models. Our evaluation of the usability of SARD and its creativity
support shows that while node-based visualization of the narrative may help
writers build a mental model, it exerts unnecessary mental overhead to the
writer and becomes a source of distraction as the story becomes more
elaborated. We also found that AI generates stories that are less lexically
diverse, irrespective of the complexity of the story. We identified some
patterns and limitations of our tool that can guide the development of future
human-AI co-writing tools.

我们提出了一个名为 SARD 的可视化工具，用于利用大型语言模型生成多章节故事，通过对其可用性和创意支持的评估，我们发现节点可视化可能有助于作者建立心理模型，但在故事更加复杂时会导致不必要的认知负荷和注意力分散，还发现无论故事复杂与否，AI 生成的故事在词汇多样性方面都较少，这些发现为未来人工智能与人类共同创作工具的发展提供了指导。

SARD: 人工智能与人类协作的故事生成

SARD: A Human-AI Collaborative Story Generation

Healthcare providers are increasingly using machine learning to predict
patient outcomes to make meaningful interventions. However, despite innovations
in this area, deep learning models often struggle to match performance of
shallow linear models in predicting these outcomes, making it difficult to
leverage such techniques in practice. In this work, motivated by the task of
clinical prediction from insurance claims, we present a new technique called
Reverse Distillation which pretrains deep models by using high-performing
linear models for initialization. We make use of the longitudinal structure of
insurance claims datasets to develop Self Attention with Reverse Distillation,
or SARD, an architecture that utilizes a combination of contextual embedding,
temporal embedding and self-attention mechanisms and most critically is trained
via reverse distillation. SARD outperforms state-of-the-art methods on multiple
clinical prediction outcomes, with ablation studies revealing that reverse
distillation is a primary driver of these improvements. Code is available at
this https URL

利用 Reverse Distillation 技术，该研究提出了一种名为 SARD 的深度学习模型，相对于现有方法，该模型在多种临床预测结果上表现更好，关键在于训练时的反向蒸馏。