The proliferation of large language models (LLMs) has revolutionized the capabilities of natural language interfaces (NLIs) for data analysis. LLMs can perform multi-step and complex reasoning to generate data insights based on users' analytic intents. However, these insights often entangle with an abundance of contexts in analytic conversations such as code, visualizations, and natural language explanations. This hinders efficient recording, organization, and navigation of insights within the current chat-based LLM interfaces. In this paper, we first conduct a formative study with eight data analysts to understand their general workflow and pain points of insight management during LLM-powered data analysis. Accordingly, we introduce InsightLens, an interactive system to overcome such challenges. Built upon an LLM-agent-based framework that automates insight recording and organization along with the analysis process, InsightLens visualizes the complex conversational contexts from multiple aspects to facilitate insight navigation. A user study with twelve data analysts demonstrates the effectiveness of InsightLens, showing that it significantly reduces users' manual and cognitive effort without disrupting their conversational data analysis workflow, leading to a more efficient analysis experience.

翻译：大语言模型（LLMs）的兴起彻底变革了用于数据分析的自然语言界面（NLIs）的能力。LLMs能够执行多步骤的复杂推理，根据用户的分析意图生成数据洞察。然而，这些洞察常常与分析对话中大量的上下文（如代码、可视化图表和自然语言解释）交织在一起。这在当前基于聊天的LLM界面中阻碍了对洞察的高效记录、组织和导航。本文首先通过对八位数据分析师进行形成性研究，以了解他们在基于LLM的数据分析过程中的一般工作流程及洞察管理的痛点。据此，我们提出了InsightLens，一个用于克服这些挑战的交互式系统。该系统构建在一个基于LLM智能体的框架之上，该框架能在分析过程中自动完成洞察的记录与组织。InsightLens从多个维度对复杂的对话上下文进行可视化，以促进洞察导航。一项涉及十二位数据分析师的用户研究证明了InsightLens的有效性，表明它能显著减少用户的手动操作和认知负担，同时不干扰其对话式数据分析工作流程，从而带来更高效的分析体验。