| Addition | Furthermore, Moreover, Additionally, In addition | Furthermore, our method generalizes well to unseen data. |
| Contrast | However, In contrast, Conversely, On the other hand, Nevertheless | However, this approach suffers from high computational cost. |
| Cause & Effect | Therefore, Consequently, As a result, Hence, Thus | Therefore, we adopt a two-stage training strategy. |
| Exemplification | For example, For instance, Specifically, In particular | Specifically, we focus on the image classification task. |
| Emphasis | Indeed, Notably, Importantly, It is worth noting that | Notably, the improvement is consistent across all datasets. |
| Concession | Although, Despite, Notwithstanding, While, Even though | Although the model is simple, it achieves competitive results. |
| Summary | In summary, To summarize, Overall, In conclusion | Overall, the proposed method outperforms existing baselines. |
| Qualification | Yet, Still, Nonetheless, That said | That said, there are several limitations to our approach. |
| Sequence | First, Second, Finally, Subsequently, Then | First, we preprocess the data. Subsequently, we train the model. |
| Condition | If, Provided that, Given that, Assuming that | Given that the dataset is imbalanced, we apply oversampling. |