← Back to all news
arXiv Computation and Language By Ilya Chekin (BroutonLab), Vyacheslav Malyugin (BroutonLab), Vladimir Chirkov (BroutonLab), Mikhail Yurushkin (Curately)

Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals

Read the original on arXiv Computation and Language →

The paper presents a system for matching job candidates to vacancies that provides interpretable evidence rather than a single relevance score. It uses a two‑stage approach: an LLM‑based labeler refined through recruiter feedback and a distilled bi‑encoder that runs online on CPU. The model, trained on 168,772 labeled pairs, achieves 95.79% agreement with recruiter‑recorded decisions on a production‑feedback subset.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv AI
Sep 7

From Matching Models to Recruiting Agents: A Systematized Narrative Review of AI Recruitment Systems, Evaluation, and Governance

The paper reviews the evolution of AI recruitment systems from simple profile matching to complex, multi‑stage workflows that retrieve evidence, compare candidates, and execute actions. It analyzes 40 representative works, highlighting transitions from similarity to reciprocal suitability, from single models to compound workflows, and from offline predictions to evidence‑aligned evaluation. The authors identify persistent gaps—such as confounded behavioral labels, limited data validity, and lack of privacy assessment—and propose a staged mapping for defensible evaluation and an agenda for auditable, evidence‑grounded systems.

By Ziyi Zhao, Guanzheng Wei
llmsagentssafety
More like this →
Hugging Face Trending Papers
Sep 3

From Matching Models to Recruiting Agents: A Systematized Narrative Review of AI Recruitment Systems, Evaluation, and Governance

The paper reviews the evolution of AI recruitment systems from simple profile matching to complex, multi‑stage workflows that retrieve evidence, compare candidates, and execute actions. It organizes 40 representative works, highlighting transitions from similarity to reciprocal suitability, from single models to compound workflows, and from offline prediction to evidence‑aligned evaluation. The authors identify persistent gaps such as confounded behavioral labels, limited data validity, hidden pipeline failures, and a lack of privacy assessment, and propose a staged mapping for defensible evaluation and an agenda for evidence‑grounded, auditable systems.

llmsagentssafety
More like this →
arXiv Computation and Language
Aug 27

Retrieve, Match, Escalate: Accurate and Scalable Product Linking with VLM-Distilled Cross-Encoders and Agentic VLMs

The paper introduces a scalable product‑linking system that uses a retrieve‑then‑match cascade. First, a lightweight text cross‑encoder auto‑resolves the majority of merchant‑catalog product pairs with high precision, while an agentic multimodal vision‑language model handles the remaining ambiguous cases by inspecting images and performing web searches. This approach balances computational cost and accuracy, improving overall link coverage from 68% to 77% without requiring fine‑tuning of the agent.

By Jian Wang, Steven Xu, Sanjyot Thete, Maryam Barouti, Tom Tang, Elaine Wu, Charu Sareen, Kyle MacDonald
llmsagentsfine-tuningmultimodal
More like this →