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arXiv · 2512.08273

AgentEval: Generative Agents as Reliable Proxies for Human Evaluation of AI-Generated Content

Thanh Vu·Richi Nayak·Thiru Balasubramaniam
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Abstract

Modern businesses are increasingly challenged by the time and expense required to generate and assess high-quality content. Human writers face time constraints, and extrinsic evaluations can be costly. While Large Language Models (LLMs) offer potential in content creation, concerns about the quality of AI-generated content persist. Traditional evaluation methods, like human surveys, further add operational costs, highlighting the need for efficient, automated solutions. This research introduces Generative Agents as a means to tackle these challenges. These agents can rapidly and cost-effectively evaluate AI-generated content, simulating human judgment by rating aspects such as coherence, interestingness, clarity, fairness, and relevance. By incorporating these agents, businesses can streamline content generation and ensure consistent, high-quality output while minimizing reliance on costly human evaluations. The study provides critical insights into enhancing LLMs for producing business-aligned, high-quality content, offering significant advancements in automated content generation and evaluation.

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Thanh Vu, Richi Nayak, Thiru Balasubramaniam. 2025-12-09. AgentEval: Generative Agents as Reliable Proxies for Human Evaluation of AI-Generated Content. https://arxiv.org/abs/2512.08273

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