Social media bot detection has always been an arms race between advancements in machine learning bot detectors and adversarial bot strategies to evade detection. In this work, we bring the arms race to the next level by investigating the opportunities and risks of state-of-the-art large language models (LLMs) in social bot detection. To investigate the opportunities, we design novel LLM-based bot detectors by proposing a mixture-of-heterogeneous-experts framework to divide and conquer diverse user information modalities. To illuminate the risks, we explore the possibility of LLM-guided manipulation of user textual and structured information to evade detection. Extensive experiments with three LLMs on two datasets demonstrate that instruction tuning on merely 1,000 annotated examples produces specialized LLMs that outperform state-of-the-art baselines by up to 9.1% on both datasets, while LLM-guided manipulation strategies could significantly bring down the performance of existing bot detectors by up to 29.6% and harm the calibration and reliability of bot detection systems.

翻译：社交媒体机器人检测始终是机器学习检测器与规避检测的对抗性机器人策略之间的军备竞赛。本研究通过探讨最先进的大型语言模型在社交机器人检测中的机遇与风险，将这场军备竞赛推向新阶段。为探究机遇，我们设计了基于LLM的新型机器人检测器，提出异构专家混合框架以实现对多样化用户信息模态的"分而治之"。为揭示风险，我们探索了LLM引导下操纵用户文本与结构化信息以规避检测的可能性。在两个数据集上使用三种LLM的广泛实验表明：仅需对1000个标注样本进行指令微调，即可训练出在两项数据集上均超越现有最佳基线模型达9.1%的专业化LLM；而LLM引导的操纵策略可使现有机器人检测器性能最大下降29.6%，并损害检测系统的校准能力与可靠性。