Abstract
Matching candidates to vacancies is central to recruitment, and a recruiter needs to see why a candidate fits, not only a single opaque relevance score. We provide this evidence as named, interpretable matching dimensions recruiters can act on - eight in our current deployment. We propose a two-part approach. The first is an LLM-based labeler whose prompts and feature definitions were refined from recruiter feedback while it served as an earlier production matching stage. In the current architecture, it is used only for offline labeling and is not called on online requests. The second is a feature bi-encoder distilled from it: a LoRA-adapted embedding backbone with compact per-dimension heads that runs on CPU and serves all online requests. Both parts keep improving: prompts are revised as feedback arrives, and the bi-encoder is retrained on the updated labels. The model is trained on 168,772 labeled vacancy-resume pairs (17,921 vacancies and 180,030 resumes). Recruiters using the service can confirm or revise surfaced feature predictions. On 927 recruiter-recorded values from this selected production-feedback subset, the deployed student agrees with the recorded decisions in 888 cases (95.79%). This is operational, non-blinded agreement rather than an independent human evaluation.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Chekin, I., Malyugin, V., Chirkov, V., & Yurushkin, M. (2026). Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals. https://omanscience.com/en/articles/building-interpretable-feature-representations-for-resume-vacancy-matching-by-distilling-production-llm-signals
MLA 9
Chekin, Ilya, et al. "Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals." https://omanscience.com/en/articles/building-interpretable-feature-representations-for-resume-vacancy-matching-by-distilling-production-llm-signals.
Chicago (author–date)
Chekin, Ilya, Vyacheslav Malyugin, Vladimir Chirkov, and Mikhail Yurushkin. 2026. "Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals." https://omanscience.com/en/articles/building-interpretable-feature-representations-for-resume-vacancy-matching-by-distilling-production-llm-signals.
Harvard
Chekin, I., Malyugin, V., Chirkov, V. and Yurushkin, M. (2026) 'Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals', Available at: https://omanscience.com/en/articles/building-interpretable-feature-representations-for-resume-vacancy-matching-by-distilling-production-llm-signals.
Vancouver
Chekin I, Malyugin V, Chirkov V, Yurushkin M. Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals. https://omanscience.com/en/articles/building-interpretable-feature-representations-for-resume-vacancy-matching-by-distilling-production-llm-signals
IEEE
I. Chekin, V. Malyugin, V. Chirkov, and M. Yurushkin, "Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals," https://omanscience.com/en/articles/building-interpretable-feature-representations-for-resume-vacancy-matching-by-distilling-production-llm-signals.