The excellent generalization, contextual learning, and emergence abilities in the pre-trained large models (PLMs) handle specific tasks without direct training data, making them the better foundation models in the adversarial domain adaptation (ADA) methods to transfer knowledge learned from the source domain to target domains. However, existing ADA methods fail to account for the confounder properly, which is the root cause of the source data distribution that differs from the target domains. This study proposes an adversarial domain adaptation with confounder balancing for PLMs fine-tuning (ADA-CBF). The ADA-CBF includes a PLM as the foundation model for a feature extractor, a domain classifier and a confounder classifier, and they are jointly trained with an adversarial loss. This loss is designed to improve the domain-invariant representation learning by diluting the discrimination in the domain classifier. At the same time, the adversarial loss also balances the confounder distribution among source and unmeasured domains in training. Compared to existing ADA methods, ADA-CBF can correctly identify confounders in domain-invariant features, thereby eliminating the confounder biases in the extracted features from PLMs. The confounder classifier in ADA-CBF is designed as a plug-and-play and can be applied in the confounder measurable, unmeasurable, or partially measurable environments. Empirical results on natural language processing and computer vision downstream tasks show that ADA-CBF outperforms the newest GPT-4, LLaMA2, ViT and ADA methods.

Attention networks often make decisions relying solely on a few pieces of tokens, even if those reliances are not truly indicative of the underlying meaning or intention of the full context. That can lead to over-fitting in Transformers and hinder their ability to generalize. Attention regularization and sparsity-based methods have been used to overcome this issue. However, these methods cannot guarantee that all tokens have sufficient receptive fields for global information inference. Thus, the impact of individual biases cannot be effectively reduced. As a result, the generalization of these approaches improved slightly from the training data to new data. To address these limitations, we proposed a balanced sparsity (BaS) regularized attention network on top of the Transformers, called BaSFormer. BaS regularization introduces the K-regular graph constraint on self-attention connections, which replaces SoftMax with SparseMax in the attention transformation. In BaS-regularized self-attentions, SparseMax assigns zero attention scores to low-scoring connections, highlighting influential and meaningful contexts. The K-regular graph constraint ensures that all tokens have an equal-sized receptive field to aggregate information, which facilitates the involvement of global tokens in the feature update of each layer and reduces the impact of individual biases. As no continuous loss can be used as the K-regular graph regularization, we proposed an exponential extremum loss with augmented Lagrangian. Experimental results show that BaSFormer improves debiasing effectiveness compared to the newest large language models, such as the chatGPT, GPT-4 and LLaMA. In addition, BaSFormer achieves new state-of-the-art results in text generation tasks. Interestingly, this paper also evaluates that BaSFormer can learn hierarchically linguistic dependencies in gradient attributions, which improves interpretability and adversarial robustness.