As generative AI becomes more prevalent, it is important to study how human users interact with such models. In this work, we investigate how people use text-to-image models to generate desired target images. To study this interaction, we created ArtWhisperer, an online game where users are given a target image and are tasked with iteratively finding a prompt that creates a similar-looking image as the target. Through this game, we recorded over 50,000 human-AI interactions; each interaction corresponds to one text prompt created by a user and the corresponding generated image. The majority of these are repeated interactions where a user iterates to find the best prompt for their target image, making this a unique sequential dataset for studying human-AI collaborations. In an initial analysis of this dataset, we identify several characteristics of prompt interactions and user strategies. People submit diverse prompts and are able to discover a variety of text descriptions that generate similar images. Interestingly, prompt diversity does not decrease as users find better prompts. We further propose a new metric to quantify the steerability of AI using our dataset. We define steerability as the expected number of interactions required to adequately complete a task. We estimate this value by fitting a Markov chain for each target task and calculating the expected time to reach an adequate score in the Markov chain. We quantify and compare AI steerability across different types of target images and two different models, finding that images of cities and natural world images are more steerable than artistic and fantasy images. These findings provide insights into human-AI interaction behavior, present a concrete method of assessing AI steerability, and demonstrate the general utility of the ArtWhisperer dataset.

翻译：随着生成式人工智能的普及，研究人类用户如何与这类模型交互变得至关重要。本文探讨了人们如何使用文生图模型生成期望的目标图像。为研究这一交互过程，我们创建了在线游戏ArtWhisperer：用户被给定目标图像，需通过迭代方式找到能生成与之相似图像的提示词。通过该游戏，我们记录了超过5万次人机交互事件；每次交互对应一条用户创建的文本提示及其生成的图像。其中大部分为重复交互——用户通过迭代寻找目标图像的最佳提示词，这使得该数据集成为研究人机协作的独特时序数据集。在对数据集的初步分析中，我们识别出提示词交互与用户策略的若干特征。用户会提交多样化的提示词，并发现多种能生成相似图像的文本描述。有趣的是，即使用户找到更优提示词，提示词的多样性并未降低。我们进一步提出基于该数据集量化AI可操控性的新指标：将可操控性定义为完成任务所需的预期交互次数。通过为每个目标任务拟合马尔可夫链并计算达到满意分数的预期时间，我们估算了该指标。我们量化并比较了不同目标图像类型及两种模型下的AI可操控性，发现城市与自然景观类图像比艺术与幻想类图像具有更高的可操控性。这些发现揭示了人机交互行为规律，提供了评估AI可操控性的具体方法，并展示了ArtWhisperer数据集的广泛适用性。