# Characterizing the dynamics of learning in repeated reference games

arXiv: https://arxiv.org/abs/1912.07199 · HTML: https://mlsanity.com/paper/1912.07199/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2019.

## Built on by (most cited first, 30 of 34)

- [Abstract Visual Reasoning with Tangram Shapes](https://mlsanity.com/paper/2211.16492/) (2022)
- [From partners to populations: A hierarchical Bayesian account of coordination and convention](https://mlsanity.com/paper/2104.05857/) (2021)
- [Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior](https://mlsanity.com/paper/2108.04812/) (2021)
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- [Learning to communicate about shared procedural abstractions](https://mlsanity.com/paper/2107.00077/) (2021)
- [Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches](https://mlsanity.com/paper/2211.08371/) (2022)
- [Analysis of Language Change in Collaborative Instruction Following](https://mlsanity.com/paper/2109.04452/) (2021)
- [MUG: Interactive Multimodal Grounding on User Interfaces](https://mlsanity.com/paper/2209.15099/) (2022)
- [Identifying concept libraries from language about object structure](https://mlsanity.com/paper/2205.05666/) (2022)
- [Towards Pragmatic Production Strategies for Natural Language Generation Tasks](https://mlsanity.com/paper/2210.12828/) (2022)
- [Post-training for Efficient Communication via Convention Formation](https://mlsanity.com/paper/2508.06482/) (2025)
- [Success and Cost Elicit Convention Formation for Efficient Communication](https://mlsanity.com/paper/2510.24023/) (2025)
- [A practical introduction to the Rational Speech Act modeling framework](https://mlsanity.com/paper/2105.09867/) (2021)
- [Retrospective Learning from Interactions](https://mlsanity.com/paper/2410.13852/) (2024)
- [Human Learning by Model Feedback: The Dynamics of Iterative Prompting with Midjourney](https://mlsanity.com/paper/2311.12131/) (2023)
- [X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback](https://mlsanity.com/paper/2203.02072/) (2022)
- [Analysing Cross-Speaker Convergence in Face-to-Face Dialogue through the Lens of Automatically Detected Shared Linguistic Constructions](https://mlsanity.com/paper/2405.08546/) (2024)
- [Characterizing Language Use in a Collaborative Situated Game](https://mlsanity.com/paper/2512.03381/) (2025)
- [Semantic uncertainty guides the extension of conventions to new referents](https://mlsanity.com/paper/2305.06539/) (2023)
- [Characterizing tradeoffs between teaching via language and demonstrations in multi-agent systems](https://mlsanity.com/paper/2305.11374/) (2023)
- [A Joint Study of Phrase Grounding and Task Performance in Vision and Language Models](https://mlsanity.com/paper/2309.02691/) (2023)
- [SceneGram: Conceptualizing and Describing Tangrams in Scene Context](https://mlsanity.com/paper/2506.11631/) (2025)
- [Visual resemblance and communicative context constrain the emergence of graphical conventions](https://mlsanity.com/paper/2109.13861/) (2021)
- [PACE: Procedural Abstractions for Communicating Efficiently](https://mlsanity.com/paper/2409.20120/) (2024)
- [A Multimodal Framework for Aligning Human Linguistic Descriptions with Visual Perceptual Data](https://mlsanity.com/paper/2602.19562/) (2026)
- [Cognitive Architecture Toward Common Ground Sharing Among Humans and Generative AIs: Trial on Model-Model Interactions in Tangram Naming Task](https://mlsanity.com/paper/2311.05851/) (2023)
- [Ad hoc conventions generalize to new referents](https://mlsanity.com/paper/2509.05566/) (2025)
- [Evaluating Spatiotemporal Consistency in Automatically Generated Sewing Instructions](https://mlsanity.com/paper/2509.24792/) (2025)
- [Aligned but Not Partner-Specific: Distinguishing How Multimodal LLM Agents Succeed in Reference Games Without Human-Like Conventions](https://mlsanity.com/paper/2606.08081/) (2026)
- [Implicit vs. Explicit Prompting Strategies for LVLMs in Referential Communication](https://mlsanity.com/paper/2606.17372/) (2026)

## Abstract (arXiv)

The language we use over the course of conversation changes as we establish common ground and learn what our partner finds meaningful. Here we draw upon recent advances in natural language processing to provide a finer-grained characterization of the dynamics of this learning process. We release an open corpus (>15,000 utterances) of extended dyadic interactions in a classic repeated reference game task where pairs of participants had to coordinate on how to refer to initially difficult-to-describe tangram stimuli. We find that different pairs discover a wide variety of idiosyncratic but efficient and stable solutions to the problem of reference. Furthermore, these conventions are shaped by the communicative context: words that are more discriminative in the initial context (i.e. that are used for one target more than others) are more likely to persist through the final repetition. Finally, we find systematic structure in how a speaker's referring expressions become more efficient over time: syntactic units drop out in clusters following positive feedback from the listener, eventually leaving short labels containing open-class parts of speech. These findings provide a higher resolution look at the quantitative dynamics of ad hoc convention formation and support further development of computational models of learning in communication.