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Dialogue participants may have varying levels of knowledge about the topicunder discussion. In such cases, it is essential for speakers to adapt theirutterances by taking their audience into account. Yet, it is an open questionhow such adaptation can be modelled in computational agents. In this paper, wemodel a visually grounded referential game between a knowledgeable speaker anda listener with more limited visual and linguistic experience. Inspired bypsycholinguistic theories, we endow our speaker with the ability to adapt itsreferring expressions via a simulation module that monitors the effectivenessof planned utterances from the listener's perspective. We propose an adaptationmechanism building on plug-and-play approaches to controlled languagegeneration, where utterance generation is steered on the fly by the simulatorwithout finetuning the speaker's underlying language model. Our results andanalyses show that our approach is effective: the speaker's utterances becomecloser to the listener's domain of expertise, which leads to highercommunicative success.

Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind