Making sense of unstructured text datasets is perennially difficult, yet increasingly relevant with Large Language Models. Data workers often rely on dataset summaries, especially distributions of various derived features. Some features, like toxicity or topics, are relevant to many datasets, but many interesting features are domain specific: instruments and genres for a music dataset, or diseases and symptoms for a medical dataset. Accordingly, data workers often run custom analyses for each dataset, which is cumbersome and difficult. We present AutoHistograms, a visualization tool leveragingLLMs. AutoHistograms automatically identifies relevant features, visualizes them with histograms, and allows the user to interactively query the dataset for categories of entities and create new histograms. In a user study with 10 data workers (n=10), we observe that participants can quickly identify insights and explore the data using AutoHistograms, and conceptualize a broad range of applicable use cases. Together, this tool and user study contributeto the growing field of LLM-assisted sensemaking tools.

翻译：理解非结构化文本数据集始终是一项艰巨任务，但随着大语言模型的发展，这一需求日益凸显。数据工作者通常依赖数据集摘要，尤其是各类派生特征的分布情况。部分特征（如毒性或主题）与许多数据集相关，但更多有趣的特征具有领域特异性：音乐数据集中的乐器和流派，或医学数据集中的疾病和症状。因此，数据工作者通常需要为每个数据集进行定制化分析，过程繁琐且困难。我们提出AutoHistograms——一种利用大语言模型的可视化工具。它可自动识别相关特征，通过直方图进行可视化，并支持用户对实体类别进行交互式查询，从而创建新的直方图。在针对10名数据工作者的用户研究中，我们发现参与者能借助AutoHistograms快速发现洞见、探索数据，并构想出广泛的应用场景。该工具及用户研究共同推动了LLM辅助意义建构工具这一新兴领域的发展。