Sensemaking tasks often entail navigating through complex, ambiguous data to construct coherent insights. Prior work has shown that crowds can effectively distribute cognitive load, pooling diverse perspectives to enhance analytical depth. Recent advancements in LLMs have further expanded the toolkit for sensemaking, offering scalable data processing, complex pattern recognition, and the ability to infer and propose meaningful hypotheses. In this study, we explore how LLMs (i.e., GPT-4) can assist in a complex sensemaking task of deciphering fictional terrorist plots. We explore two different approaches for leveraging GPT-4's capabilities: a holistic sensemaking process and a step-by-step approach. Our preliminary investigations open the doors for future research into optimizing human-AI collaborative workflows, aiming to harness the complementary strengths of both for more effective sensemaking in complex scenarios.

翻译：意义建构任务通常需要处理复杂、模糊的数据以构建连贯的见解。先前研究表明，群体能够有效分配认知负荷，汇集多元视角以提升分析深度。大语言模型（LLM）的最新进展进一步扩展了意义建构的工具集，提供了可扩展的数据处理、复杂模式识别以及推断和提出有意义假设的能力。本研究探讨了LLM（即GPT-4）如何在破译虚构恐怖主义阴谋这一复杂意义建构任务中提供协助。我们探索了两种利用GPT-4能力的不同方法：整体意义建构流程与分步式方法。我们的初步研究为未来优化人机协同工作流的研究开启了大门，旨在利用两者的互补优势，在复杂场景中实现更有效的意义建构。