The ability to produce and understand an unlimited number of different sentences is a hallmark of human language. Linguists have sought to define the essence of this generative capacity using formal grammars that describe the syntactic dependencies between constituents, independent of the computational limitations of the human brain. Here, we evaluate this independence assumption by sampling sentences uniformly from the space of possible syntactic structures. We find that the average dependency distance between syntactically related words, a proxy for memory limitations, is less than expected by chance in a collection of state-of-the-art classes of dependency grammars. Our findings indicate that memory limitations have permeated grammatical descriptions, suggesting that it may be impossible to build a parsimonious theory of human linguistic productivity independent of non-linguistic cognitive constraints.
LLMs Can't Play Hangman: On the Necessity of a Private Working Memory for Language Agents
Mila – Quebec AI Institute（魁北克人工智能研究所）；Polytechnique Montréal（蒙特利尔大学）；University of California, San Diego（加州大学圣地亚哥分校）；LAMA-WeST Lab（LAMA-WeST实验室）；Chandar Research Lab（Chandar研究实验室）
