让AI学会换位思考，提前准备专业问答
Rehearsing Answers to Probable Questions with Perspective-Taking
- 用因果知识图谱+换位思考，模拟真实职场问答场景
- 在真实高管与分析师对话数据上验证，效果显著优于基线
- 适合研究AI在商业、演讲等高阶场景中的应用
问答系统长期聚焦阅读理解与常识推理，但专业人士在正式演讲前预判并准备可能问题的场景仍被忽视。本文首次系统研究该问题，基于真实公司高管与专业分析师的问答对话数据，结合三种因果知识图谱与三种大语言模型展开实验。结果表明，引入因果知识图谱与换位思考机制，能显著提升回答的针对性与专业性，为大模型在职业化问答场景的应用提供关键洞见。
原文摘要 · Abstract (English)
Question answering (QA) has been a long-standing focus in the NLP field, predominantly addressing reading comprehension and common sense QA. However, scenarios involving the preparation of answers to probable questions during professional oral presentations remain underexplored. In this paper, we pioneer the examination of this crucial yet overlooked topic by utilizing real-world QA conversation transcripts between company managers and professional analysts. We explore the proposed task using three causal knowledge graphs (KGs) and three large language models (LLMs). This work provides foundational insights into the application of LLMs in professional QA scenarios, highlighting the importance of causal KGs and perspective-taking in generating effective responses.
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