Emergence of order in random languages
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Abstract (arXiv)
We consider languages generated by weighted context-free grammars. It is shown that the behaviour of large texts is controlled by saddle-point equations for an appropriate generating function. We then consider ensembles of grammars, in particular the Random Language Model of E. DeGiuli, Phys. Rev. Lett., 122, 128301, 2019. This model is solved in the replica-symmetric ansatz, which is valid in the high-temperature, disordered phase. It is shown that in the phase in which languages carry information, the replica symmetry must be broken.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2019.
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Citing papers per year: 2023: 2; 2024: 3.
5 papers.
- 2024Towards a theory of how the structure of language is acquired by deep neural networkscited by 38
- 2024How transformers learn structured data: insights from hierarchical filteringcited by 18
- 2024Integrating Randomness in Large Language Models: A Linear Congruential Generator Approach for Generating Clinically Relevant Contentcited by 4
- 2023Robustness of the Random Language Modelcited by 1
- 2023Strahler Number of Natural Language Sentences in Comparison with Random Treescited by 0
- 2024How transformers learn structured data: insights from hierarchical filtering27 Aug 2024
- 2024Integrating Randomness in Large Language Models: A Linear Congruential Generator Approach for Generating Clinically Relevant Content4 Jul 2024
- 2024Towards a theory of how the structure of language is acquired by deep neural networks28 May 2024
- 2023Robustness of the Random Language Model26 Sep 2023
- 2023Strahler Number of Natural Language Sentences in Comparison with Random Trees6 Jul 2023