Relevant language describing trends in data can be useful for generating summaries to help with readers' takeaways. However, the language employed in these often template-generated summaries tends to be simple, ranging from describing simple statistical information (e.g., extrema and trends) without additional context and richer language to provide actionable insights. Recent advances in Large Language Models (LLMs) have shown promising capabilities in capturing subtle nuances in language when describing information. This workshop paper specifically explores how LLMs can provide more actionable insights when describing trends by focusing on three dimensions of analytical narrative structure: semantic, rhetorical, and pragmatic. Building on prior research that examines visual and linguistic signatures for univariate line charts, we examine how LLMs can further leverage the semantic dimension of analytical narratives using quantified semantics to describe shapes in trends as people intuitively view them. These semantic descriptions help convey insights in a way that leads to a pragmatic outcome, i.e., a call to action, persuasion, warning vs. alert, and situational awareness. Finally, we identify rhetorical implications for how well these generated narratives align with the perceived shape of the data, thereby empowering users to make informed decisions and take meaningful actions based on these data insights.

翻译：描述数据趋势的相关语言有助于生成摘要，帮助读者把握要点。然而，这些通常由模板生成的摘要往往语言简单，仅描述基础统计信息（如极值和趋势），缺乏额外语境和更丰富的表达以提供可操作的见解。大语言模型的最新进展展现出在描述信息时捕捉语言细微差别的潜力。本研讨会论文聚焦分析性叙事结构的三个维度（语义、修辞和语用），专门探索大语言模型如何通过描述趋势提供更具可操作性的见解。基于先前研究对单变量折线图的视觉与语言特征的分析，我们检验了大语言模型如何进一步利用量化语义来阐述分析性叙事的语义维度，以人们直观感知的方式描述趋势形态。这些语义描述有助于传达见解，从而产生语用结果，即行动号召、说服、警告与警报及态势感知。最后，我们识别了这些生成叙事与数据感知形态匹配程度的修辞影响，从而帮助用户基于这些数据见解做出明智决策并采取有意义行动。