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

2025-12-09

AI Summary

Enterprises face a bottleneck in AI-generated content evaluation: manual assessment is labor-intensive and time-consuming, while conventional automated methods fail to replicate human multidimensional judgment. This paper proposes a Large Language Model (LLM)-based Generative Agent framework that emulates human evaluators through prompt engineering and a multi-dimensional scoring mechanism—assessing coherence, engagement, clarity, fairness, and relevance. We establish Generative Agents as the first scalable, fine-grained alternative to human evaluation. Experiments demonstrate strong agreement with human annotations (mean Spearman’s ρ > 0.85), a 12× speedup in evaluation throughput, and a 90% reduction in cost. By substantially diminishing reliance on manual annotation, our approach introduces an efficient, trustworthy, and automated quality assurance paradigm for AI content production.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Automating evaluation of AI-generated content to reduce human costs
• Simulating human judgment on coherence, fairness, and relevance aspects
• Enhancing LLMs for business-aligned, high-quality content production

Innovation

• Generative agents simulate human judgment for evaluation
• Automated assessment of coherence, clarity, and relevance aspects
• Streamlining content generation by minimizing costly human evaluations