Language models (LMs) are trained on collections of documents, written by
individual human agents to achieve specific goals in an outside world. During
training, LMs have access only to text of these documents, with no direct
evidence of the internal states of the agents that produced them -- a fact
often used to argue that LMs are incapable of modeling goal-directed aspects of
human language production and comprehension. Can LMs trained on text learn
anything at all about the relationship between language and use? I argue that
LMs are models of intentional communication in a specific, narrow sense. When
performing next word prediction given a textual context, an LM can infer and
represent properties of an agent likely to have produced that context. These
representations can in turn influence subsequent LM generation in the same way
that agents' communicative intentions influence their language. I survey
findings from the recent literature showing that -- even in today's non-robust
and error-prone models -- LMs infer and use representations of fine-grained
communicative intentions and more abstract beliefs and goals. Despite the
limited nature of their training data, they can thus serve as building blocks
for systems that communicate and act intentionally.

语言模型仅通过文本训练，但仍然可以推断和表示产生语境的人的代理人属性，包括细粒度的交际意图和抽象的信仰和目标，并被用于构建通信和行为系统。