Explainable AI methods facilitate the understanding of model behaviour, yet, small, imperceptible perturbations to inputs can vastly distort explanations. As these explanations are typically evaluated holistically, before model deployment, it is difficult to assess when a particular explanation is trustworthy. Some studies have tried to create confidence estimators for explanations, but none have investigated an existing link between uncertainty and explanation quality. We artificially simulate epistemic uncertainty in text input by introducing noise at inference time. In this large-scale empirical study, we insert different levels of noise perturbations and measure the effect on the output of pre-trained language models and different uncertainty metrics. Realistic perturbations have minimal effect on performance and explanations, yet masking has a drastic effect. We find that high uncertainty doesn't necessarily imply low explanation plausibility; the correlation between the two metrics can be moderately positive when noise is exposed during the training process. This suggests that noise-augmented models may be better at identifying salient tokens when uncertain. Furthermore, when predictive and epistemic uncertainty measures are over-confident, the robustness of a saliency map to perturbation can indicate model stability issues. Integrated Gradients shows the overall greatest robustness to perturbation, while still showing model-specific patterns in performance; however, this phenomenon is limited to smaller Transformer-based language models.
Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues
Leibniz University Hannover, Institute of Artificial Intelligence（汉诺威莱布尼茨大学人工智能研究所）；LMU Munich, MCML（慕尼黑大学MCML）；Paderborn University（帕德博恩大学）；Bielefeld University, CITEC（比勒菲尔德大学CITEC）
Can Large Language Models Still Explain Themselves? Investigating the Impact of Quantization on Self-Explanations
Quality and Usability Lab, Technische Universität Berlin（柏林技术大学质量与可用性实验室）；University of Copenhagen（哥本哈根大学）；Saarland Informatics Campus（萨尔州信息学校园）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Centre for European Research in Trusted AI (CERTAIN)（可信AI欧洲研究中心）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究院）
References Matter: Investigating the Impact of Reference Set Variation on Summarization Evaluation
MaiNLP, LMU Munich（LMU慕尼黑人工智能实验室）；Munich Center for Machine Learning (MCML), Germany（慕尼黑机器学习中心）；Department of Linguistics, University of Pittsburgh, USA（匹兹堡大学语言学系）；NLP Group, Department of Information and Computing Sciences, Utrecht University, Netherlands（乌得勒支大学信息与计算科学系自然语言处理组）
Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods
Technical University of Munich（慕尼黑技术大学）；School of Computation, Information and Technology（计算、信息与技术学院）；Department of Computer Science（计算机科学系）；Technische Universität Berlin（柏林技术大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
Explaining Graph Neural Networks for Node Similarity on Graphs
Vrije Universiteit Amsterdam（瓦赫宁根大学阿姆斯特丹）；Bosch Center for Artificial Intelligence（博世人工智能中心）；Abo Akademi University（阿博阿卡迪米大学）；Elsevier discovery lab（埃斯勒弗发现实验室）；University of Amsterdam（阿姆斯特丹大学）
