# Evolution of Efficient Symbolic Communication Codes

arXiv: https://arxiv.org/abs/2306.02383 · HTML: https://mlsanity.com/paper/2306.02383/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 4 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [BPE-Dropout: Simple and Effective Subword Regularization](https://mlsanity.com/paper/1910.13267/) (2019)
- [Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation](https://mlsanity.com/paper/2005.06606/) (2020)
- [Learning Language from a Large (Unannotated) Corpus](https://mlsanity.com/paper/1401.3372/) (2014)
- [Unsupervised Tokenization Learning](https://mlsanity.com/paper/2205.11443/) (2022)
- [Self-tuning hyper-parameters for unsupervised cross-lingual tokenization](https://mlsanity.com/paper/2303.02427/) (2023)

## Built on by (most cited first, 1 of 1)

- [Human-aligned Quantification of Numerical Data](https://mlsanity.com/paper/2511.15723/) (2025)

## Abstract (arXiv)

The paper explores how the human natural language structure can be seen as a product of evolution of inter-personal communication code, targeting maximisation of such culture-agnostic and cross-lingual metrics such as anti-entropy, compression factor and cross-split F1 score. The exploration is done as part of a larger unsupervised language learning effort, the attempt is made to perform meta-learning in a space of hyper-parameters maximising F1 score based on the "ground truth" language structure, by means of maximising the metrics mentioned above. The paper presents preliminary results of cross-lingual word-level segmentation tokenisation study for Russian, Chinese and English as well as subword segmentation or morphological parsing study for English. It is found that language structure form the word-level segmentation or tokenisation can be found as driven by all of these metrics, anti-entropy being more relevant to English and Russian while compression factor more specific for Chinese. The study for subword segmentation or morphological parsing on English lexicon has revealed straight connection between the compression been found to be associated with compression factor, while, surprising, the same connection with anti-entropy has turned to be the inverse.