Curriculum learning is a method of prioritizing learning data to improve learning performance. In this paper, we propose a new algorithm that determines how to select learning data and when to start and stop curriculum learning by considering learning errors. We use entropy to select data samples with less consistent predictions and automatically determine the warming-up period based on the characteristics of the data. Additionally, to mitigate learning bias, we introduced a variable that adjusts the range of sample selection according to the progress of the training. To validate our method, we conducted extensive experiments on both balanced and imbalanced data classification tasks, and our proposed approach showed an average improvement of about 1.8%, with a maximum improvement of up to 4.4%, compared to previously suggested methods.

GPT (Generative Pre-trained Transformer) is a generative language model that demonstrates outstanding performance in the field of text generation. Generally, the attention mechanism of the transformer model behaves similarly to a copy distribution. However, due to the absence of a dedicated encoder, it is challenging to ensure that the input is retained for generation. We propose a model that emphasizes the copy mechanism in GPT. We generate masks for the input words to initialize the distribution and explicitly encourage copying through training. To demonstrate the effectiveness of our approach, we conducted experiments to restore ellipsis and anaphora in dialogue. In a single domain, we achieved 0.4319 (BLEU), 0.6408 (Rouge-L), 0.9040 (simCSE), and 0.9070 (BERTScore), while in multi-domain settings we obtained 0.4611 (BLEU), 0.6379 (Rouge-L), 0.8902 (simCSE), and 0.8999 (BERTScore). Additionally, we evaluated the operation of the copy mechanism on out-of-domain data, yielding excellent results. We anticipate that applying the copy mechanism to GPT will be useful for utilizing language models in constrained situations.

Class imbalance remains a significant challenge in classification, often leading to poor generalization on underrepresented classes. While Oversampling methods mitigate this issue by replicating minority class instances to balance class distributions, they typically overlook the informativeness of individual samples. In this paper, we propose an entropy-guided data selection strategy that dynamically prioritizes samples exhibiting frequent prediction changes during training, that is, those with high predictive entropy. Such uncertain samples are expected to contribute more effectively to the learning process. Moreover, we incorporate a credal set-based weighting scheme that adjusts class-wise selection probabilities according to global imbalance severity, quantified using the Gini coefficient. This adjustment penalizes overrepresented classes while increasing the sampling probability of rare but uncertain examples. Experiments on benchmark datasets show that the proposed method improves overall classification performance across imbalanced data settings, while also showing a more balanced trade-off across head, body, and tail classes.