用大模型微调提升招聘信息中的技能识别准确率。
Skill-LLM: Repurposing General-Purpose LLMs for Skill Extraction
- 基于通用大模型微调专用技能提取模型
- 在基准数据集上超越现有最先进方法
- 适合招聘系统与职业匹配场景使用
从职位描述中准确提取技能对招聘至关重要，但依然面临挑战。命名实体识别（NER）是常用方法。鉴于大语言模型（LLM）在多种自然语言处理任务中的成功表现，包括命名实体识别，我们提出微调一个专用的Skill-LLM及一个轻量级模型，以提升技能提取的精度和质量。在研究中，我们使用基准数据集评估了微调后的Skill-LLM和轻量级模型，并将其性能与现有最先进（SOTA）方法进行比较。结果表明，该方法优于现有的最先进技术。
原文摘要 · Abstract (English)
Accurate skill extraction from job descriptions is crucial in the hiring process but remains challenging. Named Entity Recognition (NER) is a common approach used to address this issue. With the demonstrated success of large language models (LLMs) in various NLP tasks, including NER, we propose fine-tuning a specialized Skill-LLM and a light weight model to improve the precision and quality of skill extraction. In our study, we evaluated the fine-tuned Skill-LLM and the light weight model using a benchmark dataset and compared its performance against state-of-the-art (SOTA) methods. Our results show that this approach outperforms existing SOTA techniques.
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