SparkMe: 适应性半结构化访谈用于定性洞察发现
SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery
AI总结：
SparkMe通过多智能体LLM实现适应性半结构化访谈，提升访谈效用并挖掘更丰富的职业特定洞察。
摘要：
用户经验中的定性洞察对于指导产品和政策决策至关重要，但大规模收集此类数据受到专家时间和可用性的限制。最近的研究探索了使用大型语言模型（LLMs）来自动化访谈，但现有系统缺乏一种原则性的机制来平衡系统性覆盖预定义主题与适应性探索，或追踪后续问题、深入探讨和在对话中自然产生的主题的能力。在本工作中，我们将适应性半结构化访谈建模为面试员行为的优化问题。我们将访谈效用定义为预定义访谈主题指南的覆盖、相关新兴主题的发现和面试成本（以长度衡量）之间的权衡。基于此建模，我们引入了SparkMe，一个多智能体LLM面试员，通过模拟对话回放进行反思性规划，以选择具有高预期效用的问题。我们通过与基于LLM的受访者进行受控实验评估SparkMe，证明其在提高访谈效用方面表现优异，比最佳基线提高了4.7%的访谈主题指南覆盖，并在使用更少的对话回合的情况下引发了更丰富的新兴洞察。我们进一步在70名来自7个职业领域的参与者中验证了SparkMe对AI对其工作流程影响的影响。领域专家认为SparkMe生成的高质量适应性访谈能够揭示之前方法未涵盖的有用职业特定洞察。SparkMe的代码、数据集和评估协议作为开源在https://github.com/SALT-NLP/SparkMe上提供。
英文摘要：
Qualitative insights from user experiences are critical for informing product and policy decisions, but collecting such data at scale is constrained by the time and availability of experts to conduct semi-structured interviews. Recent work has explored using large language models (LLMs) to automate interviewing, yet existing systems lack a principled mechanism for balancing systematic coverage of predefined topics with adaptive exploration, or the ability to pursue follow-ups, deep dives, and emergent themes that arise organically during conversation. In this work, we formulate adaptive semi-structured interviewing as an optimization problem over the interviewer's behavior. We define interview utility as a trade-off between coverage of a predefined interview topic guide, discovery of relevant emergent themes, and interview cost measured by length. Based on this formulation, we introduce SparkMe, a multi-agent LLM interviewer that performs deliberative planning via simulated conversation rollouts to select questions with high expected utility. We evaluate SparkMe through controlled experiments with LLM-based interviewees, showing that it achieves higher interview utility, improving topic guide coverage (+4.7% over the best baseline) and eliciting richer emergent insights while using fewer conversational turns than prior LLM interviewing approaches. We further validate SparkMe in a user study with 70 participants across 7 professions on the impact of AI on their workflows. Domain experts rate SparkMe as producing high-quality adaptive interviews that surface helpful profession-specific insights not captured by prior approaches. The code, datasets, and evaluation protocols for SparkMe are available as open-source at https://github.com/SALT-NLP/SparkMe.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
