Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals
cs.CL
Submitted: 2026-10-02
Updated: 2026-10-02
Comments: Accepted to EMNLP 2026; 13 pages, 4 figures, 7 tables
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- LANTERN: Scalable Distillation of Large Language Models for Job-Person Fit and Explanation
- Distilling the Knowledge in a Neural Network
- Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation
- An Efficient Long-Context Ranking Architecture With Calibrated LLM Distillation: Application to Person-Job Fit
- Passage Re-ranking with BERT
- Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
- ConFit v3: Improving Resume-Job Matching with LLM-based Re-Ranking
- Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
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