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Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals — arXiv:2610.03112

Source: arXiv
Original: https://arxiv.org/abs/2610.03112
License (as archived): CC0-1.0
Archived: 2026-10-05T22:51:43.983Z
SHA-256 of the archived text: dc99ceadd6fbadb6cefab4a79605470d07ce7c76e394de493b7d6e1e20bf05d9
Stable URL of this page: https://metraipolis.com/documento/dc_daae1efee95e

How to cite

Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals — arXiv:2610.03112 — arXiv, https://arxiv.org/abs/2610.03112 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_daae1efee95e (Metraipolis archive dc_daae1efee95e, sha256 dc99ceadd6fbadb6cefab4a79605470d07ce7c76e394de493b7d6e1e20bf05d9)

BibTeX

@misc{dcdaae1efee95e,
  title = {Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals — arXiv:2610.03112},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.03112},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_daae1efee95e; sha256 dc99ceadd6fbadb6cefab4a79605470d07ce7c76e394de493b7d6e1e20bf05d9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.03112. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.03112 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-05T22:46:10.574Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).