# LLMs and people both learn to form conventions -- just not with each other

arXiv: https://arxiv.org/abs/2602.08208 · HTML: https://mlsanity.com/paper/2602.08208/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Talking About Large Language Models](https://mlsanity.com/paper/2212.03551/) (2022)
- [Large Language Models Pass the Turing Test](https://mlsanity.com/paper/2503.23674/) (2025)
- [On the generalization of language models from in-context learning and finetuning: a controlled study](https://mlsanity.com/paper/2505.00661/) (2025)
- [LVLMs and Humans Ground Differently in Referential Communication](https://mlsanity.com/paper/2601.19792/) (2026)
- [Success and Cost Elicit Convention Formation for Efficient Communication](https://mlsanity.com/paper/2510.24023/) (2025)

## Built on by (most cited first, 5 of 5)

- [GlossoGen: Emergent Language in Complex Multi-Agent LLM Interactions](https://mlsanity.com/paper/2609.01491/) (2026)
- [Implicit vs. Explicit Prompting Strategies for LVLMs in Referential Communication](https://mlsanity.com/paper/2606.17372/) (2026)
- [When Seeing Is Not Enough: Benchmarking Interactive Visual Grounding in LVLMs](https://mlsanity.com/paper/2608.23978/) (2026)
- [Which one is banana man? Evaluating vision-language models in multi-turn pragmatic interpretation](https://mlsanity.com/paper/2608.29571/) (2026)
- [Talking Past the Machine: Morality, Politeness, and Alignment in Human-AI Dialogue](https://mlsanity.com/paper/2609.21401/) (2026)

## Abstract (arXiv)

Humans align to one another in conversation -- adopting shared conventions that ease communication. We test whether LLMs form the same kinds of conventions in a multimodal communication game. Both humans and LLMs display evidence of convention-formation (increasing the accuracy and consistency of their turns while decreasing their length) when communicating in same-type dyads (humans with humans, AI with AI). However, heterogenous human-AI pairs fail -- suggesting differences in communicative tendencies. In Experiment 2, we ask whether LLMs can be induced to behave more like human conversants, by prompting them to produce superficially humanlike behavior. While the length of their messages matches that of human pairs, accuracy and lexical overlap in human-LLM pairs continues to lag behind that of both human-human and AI-AI pairs. These results suggest that conversational alignment requires more than just the ability to mimic previous interactions, but also shared interpretative biases toward the meanings that are conveyed.