Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind
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Abstract (arXiv)
Dialogue participants may have varying levels of knowledge about the topic under discussion. In such cases, it is essential for speakers to adapt their utterances by taking their audience into account. Yet, it is an open question how such adaptation can be modelled in computational agents. In this paper, we model a visually grounded referential game between a knowledgeable speaker and a listener with more limited visual and linguistic experience. Inspired by psycholinguistic theories, we endow our speaker with the ability to adapt its referring expressions via a simulation module that monitors the effectiveness of planned utterances from the listener's perspective. We propose an adaptation mechanism building on plug-and-play approaches to controlled language generation, where utterance generation is steered on the fly by the simulator without finetuning the speaker's underlying language model. Our results and analyses show that our approach is effective: the speaker's utterances become closer to the listener's domain of expertise, which leads to higher communicative success.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 35 of its 66 references that are in the map. First posted on arXiv 31 May 2023.
Builds on
35 of 66 references.
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23 more
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Built on by
Citing papers per year: 2024: 2; 2025: 3; 2026: 2.
7 papers.
- 2025Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challengescited by 31
- 2025Large Language Models as Theory of Mind Aware Generative Agents with Counterfactual Reflectioncited by 13
- 2024Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialoguecited by 11
- 2024Explicit Modelling of Theory of Mind for Belief Prediction in Nonverbal Social Interactionscited by 6
- 2025Direct Preference Optimization for Adaptive Concept-based Explanationscited by 0
- 2026Personalizing MLLMs via Reinforced Multimodal Reference Gamecited by 0
- 2026Which one is banana man? Evaluating vision-language models in multi-turn pragmatic interpretationcited by 0
- 2026Which one is banana man? Evaluating vision-language models in multi-turn pragmatic interpretation30 Aug 2026
- 2026Personalizing MLLMs via Reinforced Multimodal Reference Game27 Jun 2026
- 2025Direct Preference Optimization for Adaptive Concept-based Explanations21 May 2025
- 2025Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges17 Feb 2025
- 2025Large Language Models as Theory of Mind Aware Generative Agents with Counterfactual Reflection26 Jan 2025
- 2024Explicit Modelling of Theory of Mind for Belief Prediction in Nonverbal Social Interactions9 Jul 2024
- 2024Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue10 Feb 2024