解决简历信息提取的布局差异与大模型效率难题
Layout-Aware Parsing Meets Efficient LLMs: A Unified, Scalable Framework for Resume Information Extraction and Evaluation
- 用微调的版面解析器统一多样简历格式
- 0.6B小模型实现顶尖准确率，延迟显著降低
- 已落地阿里HR平台，支持实时简历处理
自动化简历信息提取对人才招聘规模化至关重要，但实际部署面临三大挑战：简历版面与内容极度多样化、大语言模型（LLM）成本高且延迟高，以及缺乏标准化数据集和评估工具。本文提出一个兼顾版面感知与高效性的统一框架，涵盖微调的版面解析器以统一文档格式、基于并行提示与指令微调的高效LLM提取器，以及由新基准数据集支持的两阶段自动化评估体系。大量实验表明，该框架在准确率和效率上均显著优于强基线。特别地，经微调的紧凑型0.6B LLM实现了顶级准确率，同时大幅降低推理延迟与计算开销。系统已在阿里巴巴智能HR平台全面部署，支持各业务线实时应用。
原文摘要 · Abstract (English)
Automated resume information extraction is critical for scaling talent acquisition, yet its real-world deployment faces three major challenges: the extreme heterogeneity of resume layouts and content, the high cost and latency of large language models (LLMs), and the lack of standardized datasets and evaluation tools. In this work, we present a layout-aware and efficiency-optimized framework for automated extraction and evaluation that addresses all three challenges. Our system combines a fine-tuned layout parser to normalize diverse document formats, an inference-efficient LLM extractor based on parallel prompting and instruction tuning, and a robust two-stage automated evaluation framework supported by new benchmark datasets. Extensive experiments show that our framework significantly outperforms strong baselines in both accuracy and efficiency. In particular, we demonstrate that a fine-tuned compact 0.6B LLM achieves top-tier accuracy while significantly reducing inference latency and computational cost. The system is fully deployed in Alibaba's intelligent HR platform, supporting real-time applications across its business units.
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