"You Are Rejected!": An Empirical Study of Large Language Models Taking Hiring Evaluations

• URL: http://arxiv.org/abs/2510.19167v2
• Date: Thu, 23 Oct 2025 08:28:01 GMT
• Title: "You Are Rejected!": An Empirical Study of Large Language Models Taking Hiring Evaluations
• Authors: Dingjie Fu, Dianxing Shi,
• Abstract summary: This paper investigates whether Large Language Models (LLMs) can pass hiring evaluations.<n>We employ state-of-the-art LLMs to generate responses and subsequently evaluate their performance.<n>Contrary to any prior expectation of LLMs being ideal engineers, our analysis reveals a significant inconsistency between the model-generated answers and the company-referenced solutions.
• Score: 1.1254231171451319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the proliferation of the internet and the rapid advancement of Artificial Intelligence, leading technology companies face an urgent annual demand for a considerable number of software and algorithm engineers. To efficiently and effectively identify high-potential candidates from thousands of applicants, these firms have established a multi-stage selection process, which crucially includes a standardized hiring evaluation designed to assess job-specific competencies. Motivated by the demonstrated prowess of Large Language Models (LLMs) in coding and reasoning tasks, this paper investigates a critical question: Can LLMs successfully pass these hiring evaluations? To this end, we conduct a comprehensive examination of a widely used professional assessment questionnaire. We employ state-of-the-art LLMs to generate responses and subsequently evaluate their performance. Contrary to any prior expectation of LLMs being ideal engineers, our analysis reveals a significant inconsistency between the model-generated answers and the company-referenced solutions. Our empirical findings lead to a striking conclusion: All evaluated LLMs fails to pass the hiring evaluation.

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