{"title":"Evaluating the Performance of Interpretability Methods in Text Categorization Task","authors":"A. A. Rogov, N. V. Loukachevitch","doi":"10.1134/s1995080224600699","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Neural networks are progressively assuming a larger role in individuals daily routines, as their complexity continues to grow. While the model demonstrates satisfactory performance when evaluated on the test data, it often yields unforeseen outcomes in real-world scenarios. To diagnose the source of these errors, understanding the decision-making process employed by the model becomes crucial. In this paper, we consider various methods of interpreting the BERT model in classification tasks, and also consider methods for evaluating interpretation methods using vector representations fastText, GloVe and Sentence-BERT.</p>","PeriodicalId":46135,"journal":{"name":"Lobachevskii Journal of Mathematics","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Lobachevskii Journal of Mathematics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1134/s1995080224600699","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
Neural networks are progressively assuming a larger role in individuals daily routines, as their complexity continues to grow. While the model demonstrates satisfactory performance when evaluated on the test data, it often yields unforeseen outcomes in real-world scenarios. To diagnose the source of these errors, understanding the decision-making process employed by the model becomes crucial. In this paper, we consider various methods of interpreting the BERT model in classification tasks, and also consider methods for evaluating interpretation methods using vector representations fastText, GloVe and Sentence-BERT.
期刊介绍：
Lobachevskii Journal of Mathematics is an international peer reviewed journal published in collaboration with the Russian Academy of Sciences and Kazan Federal University. The journal covers mathematical topics associated with the name of famous Russian mathematician Nikolai Lobachevsky (Lobachevskii). The journal publishes research articles on geometry and topology, algebra, complex analysis, functional analysis, differential equations and mathematical physics, probability theory and stochastic processes, computational mathematics, mathematical modeling, numerical methods and program complexes, computer science, optimal control, and theory of algorithms as well as applied mathematics. The journal welcomes manuscripts from all countries in the English language.
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