New benchmark evaluates human and LLM text-to-image prompting skills

Researchers have introduced AtelierEval, a novel benchmark designed to evaluate the proficiency of both humans and multimodal large language models (MLLMs) in generating effective text-to-image prompts. This benchmark, which includes 360 expert-crafted tasks, aims to quantify the quality of prompts used to translate user intent into detailed instructions for text-to-image systems. AtelierEval also features AtelierJudge, an agentic evaluator that correlates strongly with human expert assessments, and its experiments reveal that mimicry-based prompting may be more effective than planning-based approaches for future prompters. AI

影响 Introduces a new evaluation framework for text-to-image prompting, enabling better assessment of both human and AI prompter capabilities.

排序理由 The cluster contains an academic paper introducing a new benchmark and evaluation methodology for text-to-image prompting. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• AtelierEval
• AtelierJudge
• MLLMs

• 论文
• 产品

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →