# MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model

arXiv: https://arxiv.org/abs/2508.13676 · HTML: https://mlsanity.com/paper/2508.13676/

## 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.

## Placed in

- Mixture-of-experts / [MoE large language models](https://mlsanity.com/moe/moe-large-language-models/)

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models](https://mlsanity.com/paper/2108.08877/) (2021)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://mlsanity.com/paper/2407.19669/) (2024)
- [Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations](https://mlsanity.com/paper/2307.05722/) (2023)
- [Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach](https://mlsanity.com/paper/1812.08947/) (2018)
- [Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network](https://mlsanity.com/paper/2009.13299/) (2020)
- [Towards Deep and Representation Learning for Talent Search at LinkedIn](https://mlsanity.com/paper/1809.06473/) (2018)
- [Modeling Two-Way Selection Preference for Person-Job Fit](https://mlsanity.com/paper/2208.08612/) (2022)
- [Learning Effective Representations for Person-Job Fit by Feature Fusion](https://mlsanity.com/paper/2006.07017/) (2020)
- [Enhancing Job Recommendation through LLM-based Generative Adversarial Networks](https://mlsanity.com/paper/2307.10747/) (2023)
- [Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning](https://mlsanity.com/paper/1810.04040/) (2018)

## 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