While machine translation systems have been applied to many tasks with remarkable success, machine poetry translation has remained a challenge. This study investigates the capabilities of generative Large Language Models (LLMs) in the translation of poetry (taking Shakespeare’s 154 sonnets as an example) from English to German. For this purpose, I define metrics that assess the reproduction of the rhyme scheme and the metre of the original in a quantitative way. The results indicate that LLMs still lag behind professional human translators (especially with regard to the reproduction of the rhyme scheme), but that their performance is significantly influenced by the chosen prompt strategy. In particular, iteratively refining the result emerges as a successful strategy in terms of the reproduction of the form, but this comes at the expense of other aspects such as grammaticality and the reproduction of the meaning.

This paper investigates whether LMs recruit shared computational mechanisms for general Theory of Mind (ToM) and language-specific pragmatic reasoning in order to contribute to the general question of whether LMs may be said to have emergent "social world models", i.e., representations of mental states that are repurposed across tasks (the functional integration hypothesis). Using behavioral evaluations and causal-mechanistic experiments via functional localization methods inspired by cognitive neuroscience, we analyze LMs’ performance across seven subcategories of ToM abilities (Beaudoin et al., 2020) on a substantially larger localizer dataset than used in prior like-minded work. Results from stringent hypothesis-driven statistical testing offer suggestive evidence for the functional integration hypothesis, indicating that LMs may develop interconnected "social world models" rather than isolated competencies. This work contributes novel ToM localizer data, methodological refinements to functional localization techniques, and empirical insights into the emergence of social cognition in artificial systems.