Recursion is one of the hallmarks of human language. While many design features of language have been shown to exist in animal communication systems, recursion has not. Previous research shows that GPT-4 is the first large language model (LLM) to exhibit metalinguistic abilities (Begu\v{s}, D\k{a}bkowski, and Rhodes 2023). Here, we propose several prompt designs aimed at eliciting and analyzing recursive behavior in LLMs, both linguistic and non-linguistic. We demonstrate that when explicitly prompted, GPT-4 can both produce and analyze recursive structures. Thus, we present one of the first studies investigating whether meta-linguistic awareness of recursion -- a uniquely human cognitive property -- can emerge in transformers with a high number of parameters such as GPT-4.

翻译：递归是人类语言的标志性特征之一。尽管语言的许多设计特征已被证实在动物交流系统中存在，但递归却并非如此。此前研究表明，GPT-4是首个展现元语言能力的大型语言模型（Beguš、Dąbkowski和Rhodes，2023）。在此，我们提出若干提示设计，旨在激发并分析大型语言模型中的语言与非语言递归行为。我们证明，当得到显式提示时，GPT-4既能生成也能分析递归结构。因此，本研究作为首批探索之一，旨在考察递归这一独特人类认知属性的元语言意识，是否能在如GPT-4这类具有大量参数的Transformer模型中涌现。