The proposed approach can be extended to handle more complex logical operators and reasoning scenarios beyond simple implications by incorporating a broader range of logical constraints and operators into the training objective. Instead of focusing solely on implications, the model can be trained to reason with logical operators such as conjunction, disjunction, and negation. By formulating these complex logical structures as constraints and integrating them into the semantic loss function, the model can learn to adhere to more intricate logical rules during inference.
Additionally, the approach can be enhanced by introducing hierarchical reasoning structures that involve nested logical operations. This would enable the model to tackle multi-step reasoning tasks that require chaining together multiple logical rules to arrive at a conclusion. By training the model on a diverse set of logical constraints and scenarios, it can develop a more robust understanding of logical reasoning and improve its ability to handle complex logical operations effectively.