{"title":"Safe Pretraining of Deep Language Models in a Synthetic Pseudo-Language","authors":"T. E. Gorbacheva, I. Y. Bondarenko","doi":"10.1134/s1064562423701636","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Absract</h3><p>This paper compares the pretraining of a transformer on natural language texts and on sentences of a synthetic pseudo-language. The artificial texts are automatically generated according to the rules written in a context-free grammar. The results of fine-tuning to complete tasks of the RussianSuperGLUE project statistically reliably showed that the models had the same scores. That is, the use of artificial texts facilitates the AI safety, because it can completely control the composition of the dataset. In addition, at the pretraining stage of a RoBERTa-like model, it is enough to learn recognizing only the syntactic and morphological patterns of the language, which can be successfully created in a fairly simple way, such as a context-free grammar.</p>","PeriodicalId":531,"journal":{"name":"Doklady Mathematics","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Doklady Mathematics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1134/s1064562423701636","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
Absract
This paper compares the pretraining of a transformer on natural language texts and on sentences of a synthetic pseudo-language. The artificial texts are automatically generated according to the rules written in a context-free grammar. The results of fine-tuning to complete tasks of the RussianSuperGLUE project statistically reliably showed that the models had the same scores. That is, the use of artificial texts facilitates the AI safety, because it can completely control the composition of the dataset. In addition, at the pretraining stage of a RoBERTa-like model, it is enough to learn recognizing only the syntactic and morphological patterns of the language, which can be successfully created in a fairly simple way, such as a context-free grammar.
期刊介绍：
Doklady Mathematics is a journal of the Presidium of the Russian Academy of Sciences. It contains English translations of papers published in Doklady Akademii Nauk (Proceedings of the Russian Academy of Sciences), which was founded in 1933 and is published 36 times a year. Doklady Mathematics includes the materials from the following areas: mathematics, mathematical physics, computer science, control theory, and computers. It publishes brief scientific reports on previously unpublished significant new research in mathematics and its applications. The main contributors to the journal are Members of the RAS, Corresponding Members of the RAS, and scientists from the former Soviet Union and other foreign countries. Among the contributors are the outstanding Russian mathematicians.
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