An Efficient Long-Context Ranking Architecture With Calibrated LLM Distillation: Application to Person-Job Fit
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866908770481733632 |
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| author | Jouanneau, Warren Jouffroy, Emma Palyart, Marc |
| author_facet | Jouanneau, Warren Jouffroy, Emma Palyart, Marc |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_10321 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Efficient Long-Context Ranking Architecture With Calibrated LLM Distillation: Application to Person-Job Fit Jouanneau, Warren Jouffroy, Emma Palyart, Marc Computation and Language Information Retrieval Machine Learning Social and Information Networks 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. |
| title | An Efficient Long-Context Ranking Architecture With Calibrated LLM Distillation: Application to Person-Job Fit |
| topic | Computation and Language Information Retrieval Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2601.10321 |