MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model
On the map: Mixture-of-experts → MoE large language models
Abstract (arXiv)
To maintain the company's talent pool, recruiters need to continuously search for resumes from third-party websites (e.g., LinkedIn, Indeed). However, fetched resumes are often incomplete and inaccurate. To improve the quality of third-party resumes and enrich the company's talent pool, it is essential to conduct duplication detection between the fetched resumes and those already in the company's talent pool. Such duplication detection is challenging due to the semantic complexity, structural heterogeneity, and information incompleteness of resume texts. To this end, we propose MHSNet, an multi-level identity verification framework that fine-tunes BGE-M3 using contrastive learning. With the fine-tuned , Mixture-of-Experts (MoE) generates multi-level sparse and dense representations for resumes, enabling the computation of corresponding multi-level semantic similarities. Moreover, the state-aware Mixture-of-Experts (MoE) is employed in MHSNet to handle diverse incomplete resumes. Experimental results verify the effectiveness of MHSNet
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in MoE large language models, one of 12 directions in Mixture-of-experts. Builds on 17 of its 24 references that are in the map. First posted on arXiv 19 Aug 2025.
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17 of 24 references.
References · 17
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5 more
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- 2025AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening
- 2023Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related Tasks
- 2026SemHash-LLM: A Multi-Granularity Semantic Hashing Framework for Document Deduplication
- 2026An Efficient Long-Context Ranking Architecture With Calibrated LLM Distillation: Application to Person-Job Fit
- 2026Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation
- 2025FITRep: Attention-Guided Item Representation via MLLMs