dorsal/arxiv
View SchemaAn Efficient Long-Context Ranking Architecture With Calibrated LLM Distillation: Application to Person-Job Fit
| Authors | Warren Jouanneau, Emma Jouffroy, Marc Palyart |
|---|---|
| Categories | |
| ArXiv ID | 2601.10321vv2 |
| URL | https://arxiv.org/abs/2601.10321 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
Finding the most relevant person for a job proposal in real time is challenging, especially when resumes are long, structured, and multilingual. In this paper, we propose a re-ranking model based on a new generation of late cross-attention architecture, that decomposes both resumes and project briefs to efficiently handle long-context inputs with minimal computational overhead. To mitigate historical data biases, we use a generative large language model (LLM) as a teacher, generating fine-grained, semantically grounded supervision. This signal is distilled into our student model via an enriched distillation loss function. The resulting model produces skill-fit scores that enable consistent and interpretable person-job matching. Experiments on relevance, ranking, and calibration metrics demonstrate that our approach outperforms state-of-the-art baselines.
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"abstract": "Finding the most relevant person for a job proposal in real time is challenging, especially when resumes are long, structured, and multilingual. In this paper, we propose a re-ranking model based on a new generation of late cross-attention architecture, that decomposes both resumes and project briefs to efficiently handle long-context inputs with minimal computational overhead. To mitigate historical data biases, we use a generative large language model (LLM) as a teacher, generating fine-grained, semantically grounded supervision. This signal is distilled into our student model via an enriched distillation loss function. The resulting model produces skill-fit scores that enable consistent and interpretable person-job matching. Experiments on relevance, ranking, and calibration metrics demonstrate that our approach outperforms state-of-the-art baselines.",
"arxiv_id": "2601.10321",
"authors": [
"Warren Jouanneau",
"Emma Jouffroy",
"Marc Palyart"
],
"categories": [
"cs.CL",
"cs.IR",
"cs.LG",
"cs.SI"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "An Efficient Long-Context Ranking Architecture With Calibrated LLM Distillation: Application to Person-Job Fit",
"url": "https://arxiv.org/abs/2601.10321",
"version": "v2"
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