Proposed as a solution to the inherent black-box limitations of graph neural
networks (GNNs), post-hoc GNN explainers aim to provide precise and insightful
explanations of the behaviours exhibited by trained GNNs. Despite their recent
notable advancements in academic and industrial contexts, the robustness of
post-hoc GNN explainers remains unexplored when confronted with label noise. To
bridge this gap, we conduct a systematic empirical investigation to evaluate
the efficacy of diverse post-hoc GNN explainers under varying degrees of label
noise. Our results reveal several key insights: Firstly, post-hoc GNN
explainers are susceptible to label perturbations. Secondly, even minor levels
of label noise, inconsequential to GNN performance, harm the quality of
generated explanations substantially. Lastly, we engage in a discourse
regarding the progressive recovery of explanation effectiveness with escalating
noise levels.

基于对标签噪声的变化，对不同类型的 GNN 解释器的效力进行系统的实证调查，发现 GNN 解释器容易受到标签扰动的影响，即使是轻微的标签噪声也会明显影响解释的质量，同时研究了随着噪声水平增加解释效果的逐渐恢复过程。

关于后处理 GNN 解释器对标签噪音的鲁棒性

On the Robustness of Post-hoc GNN Explainers to Label Noise

Large language models (LLMs) have shown remarkable capacity for in-context
learning (ICL), where learning a new task from just a few training examples is
done without being explicitly pre-trained. However, despite the success of
LLMs, there has been little understanding of how ICL learns the knowledge from
the given prompts. In this paper, to make progress toward understanding the
learning behaviour of ICL, we train the same LLMs with the same demonstration
examples via ICL and supervised learning (SL), respectively, and investigate
their performance under label perturbations (i.e., noisy labels and label
imbalance) on a range of classification tasks. First, via extensive
experiments, we find that gold labels have significant impacts on the
downstream in-context performance, especially for large language models;
however, imbalanced labels matter little to ICL across all model sizes. Second,
when comparing with SL, we show empirically that ICL is less sensitive to label
perturbations than SL, and ICL gradually attains comparable performance to SL
as the model size increases.

大型语言模型（LLMs）在上下文学习（ICL）方面展示了显着的能力，在没有明确预训练的情况下，仅通过少量的训练示例学习新任务。然而，尽管 LLMs 获得了成功，对于 ICL 如何从给定的提示中学习知识却知之甚少。在本文中，为了对 ICL 的学习行为有所了解，我们通过 ICL 和监督学习 (SL) 分别使用相同的演示示例训练相同的 LLMs，并研究它们在一系列分类任务中在标签扰动（即嘈杂标签和标签不平衡）下的表现。通过广泛的实验证明，我们首先发现黄金标签对下游上下文性能有显著影响，尤其是对于大型语言模型；然而，对于所有模型大小，不平衡标签对 ICL 的影响较小。其次，通过与 SL 进行比较，我们实证表明 ICL 对标签扰动的敏感性较低，并且随着模型大小的增加，ICL 逐渐获得与 SL 相当的性能。