A key property of linguistic conventions is that they hold over an entire
community of speakers, allowing us to communicate efficiently even with people
we have never met before. At the same time, much of our language use is
partner-specific: we know that words may be understood differently by different
people based on our shared history. This poses a challenge for accounts of
convention formation. Exactly how do agents make the inferential leap to
community-wide expectations while maintaining partner-specific knowledge? We
propose a hierarchical Bayesian model to explain how speakers and listeners
solve this inductive problem. To evaluate our model's predictions, we conducted
an experiment where participants played an extended natural-language
communication game with different partners in a small community. We examine
several measures of generalization and find key signatures of both
partner-specificity and community convergence that distinguish our model from
alternatives. These results suggest that partner-specificity is not only
compatible with the formation of community-wide conventions, but may facilitate
it when coupled with a powerful inductive mechanism.

探究语言约定如何形成整个社区期望结合个体专属知识，提出了基于分层贝叶斯模型的解决方案，并进行了针对具体伙伴的自然语言沟通实验进行了模型预测的评估，结果表明伙伴特异性不仅与社区范围内的约定形成相容，而且可以与强大的归纳机制相结合，加以促进。

从合作伙伴到人群的意义泛化：层次推断支持网络上的惯例形成

Generalizing meanings from partners to populations: Hierarchical  inference supports convention formation on networks

To communicate with new partners in new contexts, humans rapidly form new
linguistic conventions. Recent neural language models are able to comprehend
and produce the existing conventions present in their training data, but are
not able to flexibly and interactively adapt those conventions on the fly as
humans do. We introduce an interactive repeated reference task as a benchmark
for models of adaptation in communication and propose a regularized continual
learning framework that allows an artificial agent initialized with a generic
language model to more accurately and efficiently communicate with a partner
over time. We evaluate this framework through simulations on COCO and in
real-time reference game experiments with human partners.

本研究提出了一个基于交互式重复参考任务的连续学习框架，以更准确和高效地与伙伴通信，并通过 COCO 的模拟和与人类伙伴的实时参考游戏实验进行了评估。