  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.
