Rehearsing Answers to Probable Questions with Perspective-Taking

TL;DR

This paper investigates how large language models can generate effective answers to probable questions in professional presentation scenarios by leveraging causal knowledge graphs and perspective-taking techniques.

Contribution

It introduces a new task of preparing answers for probable questions in professional contexts and explores the use of causal KGs and LLMs for this purpose.

Findings

Causal knowledge graphs improve answer relevance.

Perspective-taking enhances response appropriateness.

LLMs can effectively utilize KGs for professional QA.

Abstract

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.