Search arXivSearch

arXiv · 2501.12954

Punctuation patterns in "Finnegans Wake" by James Joyce are largely translation-invariant

Abstract

The complexity characteristics of texts written in natural languages are significantly related to the rules of punctuation. In particular, the distances between punctuation marks measured by the number of words quite universally follow the family of Weibull distributions known from survival analyses. However, the values of two parameters marking specific forms of these distributions distinguish specific languages. This is such a strong constraint that the punctuation distributions of texts translated from the original language into another adopt quantitative characteristics of the target language. All these changes take place within Weibull distributions such that the corresponding hazard functions are always increasing. Recent previous research shows that James Joyce's famous "Finnegans Wake" is subject to such extreme distribution from the Weibull family that the corresponding hazard function is clearly decreasing. At the same time, the distances of sentence ending punctuation marks, determining the variability of sentence length, have an almost perfect multifractal organization, so far to such an extent found nowhere else in the literature. In the present contribution based on several available translations (Dutch, French, German, Polish, Russian) of "Finnegans Wake", it is shown that the punctuation characteristics of this work remain largely translation invariant, contrary to the common cases. These observations may constitute further evidence that "Finnegans Wake" is a translinguistic work in this respect as well, in line with Joyce's original intention.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Krzysztof Bartnicki, Stanisław Drożdż, Jarosław Kwapień, Tomasz Stanisz. 2025-01-22. Punctuation patterns in "Finnegans Wake" by James Joyce are largely translation-invariant. https://doi.org/10.3390/e27020177

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

What is the Role of Small Models in the LLM Era: A Survey

Large Language Models (LLMs) have demonstrated remarkable capabilities in various reasoning tasks, which leads to the development of increasingly large models. However, scaling up model sizes results in significantly higher computational costs and energy consumption, which makes these models impractical for academic researchers and businesses with limited resources. At the same time, Small Models (SMs) are frequently used in practical settings, although their significance is currently underestimated. This raises important questions about the role of small models in the era of LLMs, a topic that has received limited attention in prior surveys. In this work, we systematically examine the relationship between LLMs and SMs from two key perspectives: Collaboration and Competition (or Complementarity). We hope this survey provides valuable insights for practitioners, fostering a deeper understanding of the contribution of small models and promoting more efficient use of computational resources.

cs.CL

SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine

Large Language Models with chain-of-thought prompting, such as OpenAI-o1, have shown impressive capabilities in natural language inference tasks. However, Multi-hop Question Answering (MHQA) remains challenging for many existing models due to issues like hallucination, error propagation, and limited context length. To address these challenges and enhance LLMs' performance on MHQA, we propose the Self-Guiding prompting Finite State Machine (SG-FSM), designed to strengthen multi-hop reasoning abilities. Unlike traditional chain-of-thought methods, SG-FSM tackles MHQA by iteratively breaking down complex questions into sub-questions, correcting itself to improve accuracy. It processes one sub-question at a time, dynamically deciding the next step based on the current context and results, functioning much like an automaton. Experiments across various benchmarks demonstrate the effectiveness of our approach, outperforming strong baselines on challenging datasets such as Musique. SG-FSM reduces hallucination, enabling recovery of the correct final answer despite intermediate errors. It also improves adherence to specified output formats, simplifying evaluation significantly.

cs.CL

A Course Intelligence Platform for Higher Education: Lessons from AI-Assisted Course Evaluation

The rapid adoption of generative AI has created new opportunities for teaching, learning, and quality assurance. Existing applications, however, remain largely student-facing, with comparatively limited attention to institution-level needs. This paper presents a course intelligence platform deployed across more than 100 universities and serving over 10,000 instructors in China. By linking competency requirements, knowledge structures, teaching activities, and assessment evidence, it establishes a shared foundation for knowledge organization, instructional design, learning assessment, and quality evaluation. The course evaluation module is examined as a representative institution-facing application of the platform, which integrates national evaluation standards, structured educational evidence, customized prompting strategies, and domain-adapted LLMs to generate quantitative scores and qualitative feedback. A case study involving 100 authentic university courses is conducted to evaluate its alignment with expert judgments and the interpretability of its outputs. Statistical analyses show substantial agreement between AI-generated assessments and expert ratings, while qualitative results highlight the credibility of the feedback. The findings further suggest that AI-assisted evaluation requires not only capable models but also structured domain knowledge and transparent criteria. In this context, human ratings should be treated as an informative reference rather than an error-free gold standard, and the objective is to achieve consistent, interpretable, and defensible judgments instead of merely replicating expert scores.

cs.CL