Many training data attribution (TDA) methods aim to estimate how a model's behavior would change if one or more data points were removed from the training set.

The data is collected in Peking University and uses the same data format as SemanticKITTI. To ensure all tasks are well-defined, we formalize consistent and compatible semantic class vocabulary across the above datasets, ensuring there is a one-to-one mapping between all semantic classes. As for ASFDA and ADA settings, we have an additional warm-up stage, i.e., the network is Both source and target data have a batch size of 16. Both training loss and validation loss consistently decrease over time, indicating effective model training. We report mIoU results across existing AL approaches in Table A3.

In particular, most existing work only theoretically explains how the attention mechanism facilitates ICL under certain data models.

Specially, rigid editing leverages geometric priors from diffusion models to achieve precise viewpoint transformations under zero-shot conditions, while non-rigid editing employs adversarial training and self-attention mechanisms for complex, topologically consistent modifications.

Thus, these methods are limited by the delays caused by straggling workers.

These methods, while effective, often involve manually intensive prompt engineering.