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Classification of Candidate Achievements in Russian Resume Using an Auto-Labeling Approach

Student: Chizhov Aleksej

Supervisor: Aziz Temirkhanov

Faculty: Faculty of Computer Science

Educational Programme: Master of Data Science (Master)

Year of Graduation: 2025

Do achievements in resumes help get more interview invitations? This hypothesis was tested on a million resumes. An approach was developed that allows for the automatic classification of candidate achievements. Manual labeling was combined with prompt tuning for a large language model (LLM), which significantly reduced the costs of data labeling for training a small model that can be realistically used to process a very large number of resumes. The results of the work not only prove that achievements in resumes matter but also show which LLMs are best for auto-labeling and which models to use for deploying high-load services.

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