统计偏差让Transformer误判泛化能力，实则难以应对分布外数据
Generalization vs. Memorization in the Presence of Statistical Biases in Transformers
- 在合成算法任务中引入统计偏差，测试模型泛化表现
- 含偏差时模型表现优异，但分布外性能显著下降
- 模型依赖虚假相关性推理，适合研究泛化瓶颈的读者
本研究旨在探究统计偏差如何影响Transformer模型在算法任务上对分布内与分布外数据的泛化能力。已有研究表明，Transformer可能无意中依赖这些伪相关性，从而高估其泛化能力。为此，我们在多个合成算法任务上评估Transformer模型，系统性地引入并调整统计偏差。同时分析了Transformer不同组件对泛化的影响。结果表明，统计偏差会削弱模型在分布外数据上的表现，导致对其泛化能力的高估。模型在包含偏差的任务中表现良好，说明其严重依赖这些伪相关性进行推理。
原文摘要 · Abstract (English)
This study aims to understand how statistical biases affect the model's ability to generalize to in-distribution and out-of-distribution data on algorithmic tasks. Prior research indicates that transformers may inadvertently learn to rely on these spurious correlations, leading to an overestimation of their generalization capabilities. To investigate this, we evaluate transformer models on several synthetic algorithmic tasks, systematically introducing and varying the presence of these biases. We also analyze how different components of the transformer models impact their generalization. Our findings suggest that statistical biases impair the model's performance on out-of-distribution data, providing a overestimation of its generalization capabilities. The models rely heavily on these spurious correlations for inference, as indicated by their performance on tasks including such biases.
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