MSLEF: Multi-Segment LLM Ensemble Finetuning in Recruitment

Omar Walid; Mohamed T. Younes; Khaled Shaban; Mai Hassan; Ali Hamdi

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arXiv · 2509.06200

MSLEF: Multi-Segment LLM Ensemble Finetuning in Recruitment

Omar Walid·Mohamed T. Younes·Khaled Shaban·Mai Hassan·Ali Hamdi

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Abstract

This paper presents MSLEF, a multi-segment ensemble framework that employs LLM fine-tuning to enhance resume parsing in recruitment automation. It integrates fine-tuned Large Language Models (LLMs) using weighted voting, with each model specializing in a specific resume segment to boost accuracy. Building on MLAR , MSLEF introduces a segment-aware architecture that leverages field-specific weighting tailored to each resume part, effectively overcoming the limitations of single-model systems by adapting to diverse formats and structures. The framework incorporates Gemini-2.5-Flash LLM as a high-level aggregator for complex sections and utilizes Gemma 9B, LLaMA 3.1 8B, and Phi-4 14B. MSLEF achieves significant improvements in Exact Match (EM), F1 score, BLEU, ROUGE, and Recruitment Similarity (RS) metrics, outperforming the best single model by up to +7% in RS. Its segment-aware design enhances generalization across varied resume layouts, making it highly adaptable to real-world hiring scenarios while ensuring precise and reliable candidate representation.

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Omar Walid, Mohamed T. Younes, Khaled Shaban, Mai Hassan, Ali Hamdi. 2025-09-07. MSLEF: Multi-Segment LLM Ensemble Finetuning in Recruitment. https://arxiv.org/abs/2509.06200

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