LLMs have shown promise in replicating human-like behavior in crowdsourcing tasks that were previously thought to be exclusive to human abilities. However, current efforts focus mainly on simple atomic tasks. We explore whether LLMs can replicate more complex crowdsourcing pipelines. We find that modern LLMs can simulate some of crowdworkers' abilities in these "human computation algorithms," but the level of success is variable and influenced by requesters' understanding of LLM capabilities, the specific skills required for sub-tasks, and the optimal interaction modality for performing these sub-tasks. We reflect on human and LLMs' different sensitivities to instructions, stress the importance of enabling human-facing safeguards for LLMs, and discuss the potential of training humans and LLMs with complementary skill sets. Crucially, we show that replicating crowdsourcing pipelines offers a valuable platform to investigate (1) the relative strengths of LLMs on different tasks (by cross-comparing their performances on sub-tasks) and (2) LLMs' potential in complex tasks, where they can complete part of the tasks while leaving others to humans.

翻译：LLM在复制先前被认为人类专属的众包任务中展现出类人行为的前景。然而，当前研究主要聚焦于简单原子任务。我们探索了LLM能否复制更复杂的众包流水线。研究发现，现代LLM能够模拟众包工作者在这些"人类计算算法"中的部分能力，但成功程度存在差异，并受以下因素影响：请求者对LLM能力的理解、子任务所需的特定技能，以及执行这些子任务的最佳交互模式。我们反思了人类与LLM对指令的不同敏感性，强调了为LLM启用面向人类的安全防护的重要性，并探讨了训练人类与LLM互补技能集的潜力。关键的是，我们证明复制众包流水线提供了一个有价值的平台，可用于研究：（1）LLM在不同任务上的相对优势（通过交叉比较子任务表现）；（2）LLM在复杂任务中的潜力——它们可完成部分任务，而将其他任务留给人处理。