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[2602.13194] Semantic Chunking and the Entropy of Natural Language
Summary
This article presents a statistical model for semantic chunking in natural language, revealing insights into the entropy of English and its redundancy, with implications for large language models.
Why It Matters
Understanding the entropy of natural language is crucial for improving language models and enhancing their efficiency. This research provides a foundational model that can help in analyzing and optimizing text processing, which is significant for advancements in NLP and AI applications.
Key Takeaways
- The estimated entropy rate of printed English is about one bit per character, indicating high redundancy.
- A new model captures the multi-scale structure of natural language through semantic chunking.
- The model's predictions align with the estimated entropy rate of English, suggesting its validity.
- Entropy rates may increase with the semantic complexity of the text, as captured by the model's parameters.
- This research has implications for the development and efficiency of large language models.
Computer Science > Computation and Language arXiv:2602.13194 (cs) [Submitted on 13 Feb 2026] Title:Semantic Chunking and the Entropy of Natural Language Authors:Weishun Zhong, Doron Sivan, Tankut Can, Mikhail Katkov, Misha Tsodyks View a PDF of the paper titled Semantic Chunking and the Entropy of Natural Language, by Weishun Zhong and 4 other authors View PDF HTML (experimental) Abstract:The entropy rate of printed English is famously estimated to be about one bit per character, a benchmark that modern large language models (LLMs) have only recently approached. This entropy rate implies that English contains nearly 80 percent redundancy relative to the five bits per character expected for random text. We introduce a statistical model that attempts to capture the intricate multi-scale structure of natural language, providing a first-principles account of this redundancy level. Our model describes a procedure of self-similarly segmenting text into semantically coherent chunks down to the single-word level. The semantic structure of the text can then be hierarchically decomposed, allowing for analytical treatment. Numerical experiments with modern LLMs and open datasets suggest that our model quantitatively captures the structure of real texts at different levels of the semantic hierarchy. The entropy rate predicted by our model agrees with the estimated entropy rate of printed English. Moreover, our theory further reveals that the entropy rate of natural language is not fix...