AgentEval: Generative Agents as Reliable Proxies for Human Evaluation of AI-Generated Content. Vu, T., Nayak, R., & Balasubramaniam, T. December, 2025. arXiv:2512.08273 [cs]![AgentEval: Generative Agents as Reliable Proxies for Human Evaluation of AI-Generated Content [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@misc{vu_agenteval_2025,
title = {{AgentEval}: {Generative} {Agents} as {Reliable} {Proxies} for {Human} {Evaluation} of {AI}-{Generated} {Content}},
shorttitle = {{AgentEval}},
url = {http://arxiv.org/abs/2512.08273},
doi = {10.48550/arXiv.2512.08273},
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.},
urldate = {2026-02-05},
publisher = {arXiv},
author = {Vu, Thanh and Nayak, Richi and Balasubramaniam, Thiru},
month = dec,
year = {2025},
note = {arXiv:2512.08273 [cs]},
keywords = {Computer Science - Artificial Intelligence},
}
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