Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification

• URL: http://arxiv.org/abs/2303.07142v3
• Date: Tue, 18 Apr 2023 09:58:53 GMT
• Title: Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification
• Authors: Benjamin Clavi\'e and Alexandru Ciceu and Frederick Naylor and Guillaume Souli\'e and Thomas Brightwell
• Abstract summary: This case study investigates the task of job classification in a real-world setting. The goal is to determine whether an English-language job posting is appropriate for a graduate or entry-level position.
• Score: 58.720142291102135
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This case study investigates the task of job classification in a real-world setting, where the goal is to determine whether an English-language job posting is appropriate for a graduate or entry-level position. We explore multiple approaches to text classification, including supervised approaches such as traditional models like Support Vector Machines (SVMs) and state-of-the-art deep learning methods such as DeBERTa. We compare them with Large Language Models (LLMs) used in both few-shot and zero-shot classification settings. To accomplish this task, we employ prompt engineering, a technique that involves designing prompts to guide the LLMs towards the desired output. Specifically, we evaluate the performance of two commercially available state-of-the-art GPT-3.5-based language models, text-davinci-003 and gpt-3.5-turbo. We also conduct a detailed analysis of the impact of different aspects of prompt engineering on the model's performance. Our results show that, with a well-designed prompt, a zero-shot gpt-3.5-turbo classifier outperforms all other models, achieving a 6% increase in Precision@95% Recall compared to the best supervised approach. Furthermore, we observe that the wording of the prompt is a critical factor in eliciting the appropriate "reasoning" in the model, and that seemingly minor aspects of the prompt significantly affect the model's performance.

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