发现清洁数据中也存在虚假关联，提出防御方法提升模型可解释性
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets
- 构建合作博弈框架，让生成器选关键片段，预测器据此分类
- 实验表明该框架会引入错误关联，导致模型依赖无关特征
- 通过对抗攻击检测并消除虚假关联，适用于需可信推理的场景
本研究探讨了基于合作博弈的自解释框架：生成器从原始输入中提取最相关信息段，预测器基于该子集进行分类，二者协同训练以最大化准确率。我们首次揭示该框架可能在推理过程中引入采样偏差——即使原始数据中候选理由与标签语义无关，生成器仍可能无意间建立错误关联。通过理论分析与实证证据，我们阐明了这一偏差的成因，并提出基于对抗攻击的检测机制，进而设计指令以阻止预测器学习此类虚假相关性。在六个文本分类数据集和两个图分类数据集上，使用GRU、BERT和GCN三种架构的实验表明，所提方法不仅显著优于现有理性化方法，且性能媲美甚至超过代表性大模型llama3.1-8b-instruct。
原文摘要 · Abstract (English)
This study investigates the self-rationalization framework constructed with a cooperative game, where a generator initially extracts the most informative segment from raw input, and a subsequent predictor utilizes the selected subset for its input. The generator and predictor are trained collaboratively to maximize prediction accuracy. In this paper, we first uncover a potential caveat: such a cooperative game could unintentionally introduce a sampling bias during rationale extraction. Specifically, the generator might inadvertently create an incorrect correlation between the selected rationale candidate and the label, even when they are semantically unrelated in the original dataset. Subsequently, we elucidate the origins of this bias using both detailed theoretical analysis and empirical evidence. Our findings suggest a direction for inspecting these correlations through attacks, based on which we further introduce an instruction to prevent the predictor from learning the correlations. Through experiments on six text classification datasets and two graph classification datasets using three network architectures (GRUs, BERT, and GCN), we show that our method not only significantly outperforms recent rationalization methods, but also achieves comparable or even better results than a representative LLM (llama3.1-8b-instruct).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。