Comparison of Language Models in Skills Extraction From Vacancies and Resumes
The ability of large language models (LLMs) to “understand” large volumes of text data allows for consistent quality selection of candidates for company openings. The purpose of the work is to consider the capabilities of language models (LLM) and the specified method using vector representations in...
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| Language: | Russian |
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The Fund for Promotion of Internet media, IT education, human development «League Internet Media»
2024-03-01
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| Series: | Современные информационные технологии и IT-образование |
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| Online Access: | http://sitito.cs.msu.ru/index.php/SITITO/article/view/1078 |
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| author | Lyubov Komarova Vladimir Soloviev Alexey Kolosov |
| author_facet | Lyubov Komarova Vladimir Soloviev Alexey Kolosov |
| author_sort | Lyubov Komarova |
| collection | DOAJ |
| description | The ability of large language models (LLMs) to “understand” large volumes of text data allows for consistent quality selection of candidates for company openings. The purpose of the work is to consider the capabilities of language models (LLM) and the specified method using vector representations in the tasks of extracting skills from texts of vacancies and resumes. Particular attention is paid to the use of skill ranking methods using LLM and the following method using the cosine distance between vector representations of skills. The study consisted of three experiments: the first experiment aimed to extract skill phrases from described work experiences from a text resume; the second involves assigning skills from the resume text to a reference set of functions from the job requirements; The third experiment aims to evaluate the best performance between the two skill sets. The result of the study is the selection of the best model and the derivation of functions from the summary text, as well as a comparison of the two sets of functions with each other. Experiments have shown that language models are superior to numerical methods in terms of accuracy and flexibility in determining the capabilities of a text. Using LLM to rank features using cosine distance has shown poor performance and accuracy in measuring features between job openings and resumes. However, the numerical method using vector representation methods showed better results in quality ranking and stability with increasing number of parliamentary examples. The results of this study have practical implications for the development of more accurate and efficient personnel selection systems. The introduction of language models into human resource management processes can improve the quality and speed of processing large volumes of data, which will lead to a more accurate and faster selection of qualified specialists. |
| format | Article |
| id | doaj-art-a2091f488c65497696c23970893ddfa8 |
| institution | DOAJ |
| issn | 2411-1473 |
| language | Russian |
| publishDate | 2024-03-01 |
| publisher | The Fund for Promotion of Internet media, IT education, human development «League Internet Media» |
| record_format | Article |
| series | Современные информационные технологии и IT-образование |
| spelling | doaj-art-a2091f488c65497696c23970893ddfa82025-08-20T03:08:21ZrusThe Fund for Promotion of Internet media, IT education, human development «League Internet Media»Современные информационные технологии и IT-образование2411-14732024-03-0120115716310.25559/SITITO.020.202401.157-163Comparison of Language Models in Skills Extraction From Vacancies and ResumesLyubov Komarova0https://orcid.org/0000-0001-5277-8234Vladimir Soloviev1https://orcid.org/0000-0003-0338-1227Alexey Kolosov2https://orcid.org/0000-0002-9474-9666Financial University under the Government of the Russian Federation, Moscow, RussiaFinancial University under the Government of the Russian Federation, Moscow, RussiaLomonosov Moscow State University, Moscow, RussiaThe ability of large language models (LLMs) to “understand” large volumes of text data allows for consistent quality selection of candidates for company openings. The purpose of the work is to consider the capabilities of language models (LLM) and the specified method using vector representations in the tasks of extracting skills from texts of vacancies and resumes. Particular attention is paid to the use of skill ranking methods using LLM and the following method using the cosine distance between vector representations of skills. The study consisted of three experiments: the first experiment aimed to extract skill phrases from described work experiences from a text resume; the second involves assigning skills from the resume text to a reference set of functions from the job requirements; The third experiment aims to evaluate the best performance between the two skill sets. The result of the study is the selection of the best model and the derivation of functions from the summary text, as well as a comparison of the two sets of functions with each other. Experiments have shown that language models are superior to numerical methods in terms of accuracy and flexibility in determining the capabilities of a text. Using LLM to rank features using cosine distance has shown poor performance and accuracy in measuring features between job openings and resumes. However, the numerical method using vector representation methods showed better results in quality ranking and stability with increasing number of parliamentary examples. The results of this study have practical implications for the development of more accurate and efficient personnel selection systems. The introduction of language models into human resource management processes can improve the quality and speed of processing large volumes of data, which will lead to a more accurate and faster selection of qualified specialists.http://sitito.cs.msu.ru/index.php/SITITO/article/view/1078llmskill extractionjob matchingsemantic analysisrecruitment automation |
| spellingShingle | Lyubov Komarova Vladimir Soloviev Alexey Kolosov Comparison of Language Models in Skills Extraction From Vacancies and Resumes Современные информационные технологии и IT-образование llm skill extraction job matching semantic analysis recruitment automation |
| title | Comparison of Language Models in Skills Extraction From Vacancies and Resumes |
| title_full | Comparison of Language Models in Skills Extraction From Vacancies and Resumes |
| title_fullStr | Comparison of Language Models in Skills Extraction From Vacancies and Resumes |
| title_full_unstemmed | Comparison of Language Models in Skills Extraction From Vacancies and Resumes |
| title_short | Comparison of Language Models in Skills Extraction From Vacancies and Resumes |
| title_sort | comparison of language models in skills extraction from vacancies and resumes |
| topic | llm skill extraction job matching semantic analysis recruitment automation |
| url | http://sitito.cs.msu.ru/index.php/SITITO/article/view/1078 |
| work_keys_str_mv | AT lyubovkomarova comparisonoflanguagemodelsinskillsextractionfromvacanciesandresumes AT vladimirsoloviev comparisonoflanguagemodelsinskillsextractionfromvacanciesandresumes AT alexeykolosov comparisonoflanguagemodelsinskillsextractionfromvacanciesandresumes |