We introduce MetaphOrig, a small dataset comprising two genre-specific collections of spatial descriptions for the study of linguistic creativity and Non-Literal Expressions (NLEs). The sentence-level spatial descriptions were extracted from two distinct genre- and time-specific source corpora. Both source corpora comprise German texts: literary prose from the 18th to 20th century (KOLIMO) and factual travel reports from the 21st century (Wikivoyage). Along with the spatial descriptions, the dataset contains sentence-level originality ratings obtained through crowdsourcing and from four different LLMs (GPT-5, Qwen2.5-32B-Instruct, Mistral-Small-3.2-24B-Instruct, and Llama-3.2-3B), and word-level metaphor annotations. We provide the MetaphOrig datasets, including all annotations, to the community. The subsets can be used for further research on linguistic creativity or metaphor, either in one specific textual domain or comparatively across the two domains. We conduct an illustrative study on the dataset, treating originality as a proxy of textual creativity. In both subsets, we investigate potential correlations between sentence-level originality ratings and the density of metaphorical expressions within each sentence. We find the correlation to be present only in the KOLIMO subset. A comparison of human and LLM originality ratings shows that this pattern holds for both types of ratings.

Novel metaphor comprehension involves complex semantic processes and linguistic creativity, making it an interesting task for studying language models (LMs). This study investigates whether surprisal, a probabilistic measure of predictability in LMs, correlates with annotations of metaphor novelty in different datasets. We analyse the surprisal of metaphoric words in corpus-based and synthetic metaphor datasets using 16 causal LM variants. We propose a cloze-style surprisal method that conditions on full-sentence context. Results show that LM surprisal yields significant moderate correlations with scores/labels of metaphor novelty. We further identify divergent scaling patterns: on corpus-based data, correlation strength decreases with model size (inverse scaling effect), whereas on synthetic data it increases (quality–power hypothesis). We conclude that while surprisal can partially account for annotations of metaphor novelty, it remains limited as a metric of linguistic creativity. Code and data are publicly available: https://github.com/OmarMomen14/surprisal-metaphor-novelty

Descriptions are a central component of literary texts, yet their systematic identification remains a challenge. This work suggests an approach to identifying sentences describing spatial conditions in literary text. It was developed iteratively on German literary text and extended to non-literary text to evaluate its applicability across textual domains. To assess the robustness of the method, we involved both humans and a selection of state-of-the-art Large Language Models (LLMs) in annotating a collection of sentences regarding their descriptiveness and spatiality. We compare the annotations across human annotators and between humans and LLMs. The main contributions of this paper are: (1) a set of annotation guidelines for identifying spatial descriptions in literary texts, (2) a curated dataset of almost 4,700 annotated sentences of which around 500 are spatial descriptions, produced through in-depth discussion and consensus among annotators, and (3) a pilot study of automating the task of spatial description annotation of German texts. We publish the codes and all human and LLM annotations for the public to be used for research purposes only.