Improving Interpersonal Communication by Simulating Audiences with Language Models

TL;DR

This paper introduces the EGS framework that leverages large language models to simulate audiences, helping individuals craft more effective communication by exploring, generating, and evaluating responses in goal-oriented scenarios.

Contribution

The paper presents a novel framework, EGS, that uses LLM simulations to improve interpersonal communication by exploring diverse advice, generating communication candidates, and simulating audience reactions.

Findings

EGS outperforms popular generation methods in preference tests.

Audience simulations show high agreement with human ratings in most scenarios.

Framework successfully applied to real-world user scenarios on web forums.

Abstract

How do we communicate with others to achieve our goals? We use our prior experience or advice from others, or construct a candidate utterance by predicting how it will be received. However, our experiences are limited and biased, and reasoning about potential outcomes can be difficult and cognitively challenging. In this paper, we explore how we can leverage Large Language Model (LLM) simulations to help us communicate better. We propose the Explore-Generate-Simulate (EGS) framework, which takes as input any scenario where an individual is communicating to an audience with a goal they want to achieve. EGS (1) explores the solution space by producing a diverse set of advice relevant to the scenario, (2) generates communication candidates conditioned on subsets of the advice, and (3) simulates the reactions from various audiences to determine both the best candidate and advice to use. We evaluate the framework on eight scenarios spanning the ten fundamental processes of interpersonal communication. For each scenario, we collect a dataset of human evaluations across candidates and baselines, and showcase that our framework's chosen candidate is preferred over popular generation mechanisms including Chain-of-Thought. We also find that audience simulations achieve reasonably high agreement with human raters across 5 of the 8 scenarios. Finally, we demonstrate the generality of our framework by applying it to real-world scenarios described by users on web forums. Through evaluations and demonstrations, we show that EGS enhances the effectiveness and outcomes of goal-oriented communication across a variety of situations, thus opening up new possibilities for the application of large language models in revolutionizing communication and decision-making processes.