机器人与捷径：大型语言模型如何帮助处理未知不变性OOD场景？
Bot Meets Shortcut: How Can LLMs Aid in Handling Unknown Invariance OOD Scenarios?
AI总结：
本文研究了现有社交机器人检测器在真实场景中的鲁棒性不足问题，提出基于大语言模型的缓解策略，通过对抗数据增强提升模型在捷径学习场景下的性能。
摘要：
尽管现有社交机器人检测器在基准测试中表现良好，但其在多样化真实场景中的鲁棒性仍受限于模糊的真实标准和多变的误导性线索。特别是，shortcut learning的影响，即模型依赖于虚假的相关性而非捕捉因果任务相关特征，受到较少关注。为解决这一差距，我们深入研究了基于文本特征的检测器如何受到潜在捷径的影响，通过构建用户标签与表面文本线索之间的虚假关联来设计一系列捷径场景，以评估模型的鲁棒性。结果表明，无关特征分布的变化显著降低了社交机器人检测器的性能，基准模型的平均相对准确率下降了32%。为应对这一挑战，我们提出基于大语言模型的缓解策略，利用反事实数据增强。这些方法从数据和模型两个层面跨三个层面进行缓解，包括个体用户文本和整体数据集层面的数据分布，以及模型提取因果信息的能力。我们的策略在捷径场景下实现了平均相对性能提升56%。
英文摘要：
While existing social bot detectors perform well on benchmarks, their robustness across diverse real-world scenarios remains limited due to unclear ground truth and varied misleading cues. In particular, the impact of shortcut learning, where models rely on spurious correlations instead of capturing causal task-relevant features, has received limited attention. To address this gap, we conduct an in-depth study to assess how detectors are influenced by potential shortcuts based on textual features, which are most susceptible to manipulation by social bots. We design a series of shortcut scenarios by constructing spurious associations between user labels and superficial textual cues to evaluate model robustness. Results show that shifts in irrelevant feature distributions significantly degrade social bot detector performance, with an average relative accuracy drop of 32\% in the baseline models. To tackle this challenge, we propose mitigation strategies based on large language models, leveraging counterfactual data augmentation. These methods mitigate the problem from data and model perspectives across three levels, including data distribution at both the individual user text and overall dataset levels, as well as the model's ability to extract causal information. Our strategies achieve an average relative performance improvement of 56\% under shortcut scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
