Speaker
Description
In today's recruitment field, accurately extracting information from resumes is crucial. This paper looks at how three small language models—Llama 2, Llama 3, and Phi-3—can help with this task using a Zero-Shot approach. We checked how well these models perform by comparing their results with a hand-made dataset, focusing on accuracy and the time each model takes to run on computers that small businesses typically use. Our tests showed that even with different rules for resume data in various countries, these small, local models work well and can be used by small companies on their own equipment. We used a simple prompt in our tests, and the models performed reliably, proving their usefulness in real-world hiring situations. Our results show that small language models like Llama 2, Llama 3, and Phi-3 can accurately and efficiently extract information from resumes, helping small businesses handle resume data according to local regulations. This study highlights how these models can improve the job-matching process for smaller companies.
