Text-to-audio generation models (TAG) have achieved significant advances in generating audio conditioned on text descriptions. However, a critical challenge lies in the lack of transparency regarding how each textual input impacts the generated audio. To address this issue, we introduce AudioGenX, an Explainable AI (XAI) method that provides explanations for text-to-audio generation models by highlighting the importance of input tokens. AudioGenX optimizes an Explainer by leveraging factual and counterfactual objective functions to provide faithful explanations at the audio token level. This method offers a detailed and comprehensive understanding of the relationship between text inputs and audio outputs, enhancing both the explainability and trustworthiness of TAG models. Extensive experiments demonstrate the effectiveness of AudioGenX in producing faithful explanations, benchmarked against existing methods using novel evaluation metrics specifically designed for audio generation tasks.

Diffusion models generate high-quality images through progressive denoising but are computationally intensive due to large model sizes and repeated sampling. Knowledge distillation—transferring knowledge from a complex teacher to a simpler student model—has been widely studied in recognition tasks, particularly for transferring concepts unseen during student training. However, its application to diffusion models remains underexplored, especially in enabling student models to generate concepts not covered by the training images. In this work, we propose Random Conditioning, a novel approach that pairs noised images with randomly selected text conditions to enable efficient, image-free knowledge distillation. By leveraging this technique, we show that the student can generate concepts unseen in the training images. When applied to conditional diffusion model distillation, our method allows the student to explore the condition space without generating condition-specific images, resulting in notable improvements in both generation quality and efficiency. This promotes resource-efficient deployment of generative diffusion models, broadening their accessibility for both research and real-world applications. Code, models, and datasets are available at: https://dohyun-as.github.io/Random-Conditioning

Explaining graphs and their target Graph Neural Networks (GNNs) has gained attention with the growing use of GNNs. Most existing explainable AI (XAI) methods for GNNs focus on extracting an explanation subgraph and assume the target GNN is supervised with accessible class probabilities. However, the growing prevalence of GNN models in unsupervised settings underscores the necessity for task-irrelevant explanations. Moreover, most existing studies scarcely explore whether identifying edges absent from the original graph can improve explanation quality. To this end, we propose HINT-G (Harnessing INfluence function for Task-irrelevant explanation on Graph neural networks), a method that uses influence functions to explain models across diverse learning paradigms and considers edges beyond the given graph. The influence of an edge can be determined directly or by aggregating the influence scores of its constituent nodes, while the influence of a non-existent edge can also be determined. Furthermore, this method is task-irrelevant, since the influence score can be obtained whenever the loss function of the target model is differentiable. Experimental results on several datasets consistently demonstrate that HINT-G effectively explains graphs through the influence function framework. Our implementation code is available at https://github.com/cycy-kim/HINT-G.

Node representation learning, such as Graph Neural Networks (GNNs), has become one of the important learning methods in machine learning, and the demand for reliable explanation generation is growing. Despite extensive research on explanation generation for supervised node representation learning, explaining unsupervised models has been less explored. To address this gap, we propose a method for generating counterfactual (CF) explanations in unsupervised node representation learning, aiming to identify the most important subgraphs that cause a significant change in the $k$-nearest neighbors of a node of interest in the learned embedding space upon perturbation. The $k$-nearest neighbor-based CF explanation method provides simple, yet pivotal, information for understanding unsupervised downstream tasks, such as top-$k$ link prediction and clustering. Furthermore, we introduce a Monte Carlo Tree Search (MCTS)-based explainability method for generating expressive CF explanations for **U**nsupervised **N**ode **R**epresentation learning methods, which we call **UNR-Explainer**. The proposed method demonstrates improved performance on six datasets for both unsupervised GraphSAGE and DGI.