| Abstract |
The paper will be focusing on combining sophisticated reinforcement learning approaches with natural language processing (NLP) platforms to step up the accuracy, efficiency, and adaptability of the linguistic models. The methodology approach is based on stepwise creation of efficient basic model architectures which is combined with formulation of tasks as RL problems and careful selection of appropriate algorithms and training processes. In our study on the development of an advanced AI model which took varying evaluation metrics, it is now apparent through the comprehensive evaluation of the same, that our model's performance on different evaluation metrics was significantly better than that of the baseline approaches. For instance, this technique leads to better precision and operation speed, allowing NLP systems to undertake more complex linguistic tasks with higher rate of precision and faster inferential speeds. Furthermore, this approach facilitates the model adaptation process which helps in accommodating a hybrid intelligence environment where models are able to evolve in changing language contexts and domains. Interpretability assessment is focused on producing not only the transparency of the described method but also the ability for the user to understand the reasoning of the model and the decision-making processes. Smart cities also undertake a set of ethical considerations that encompass the significance of regards to bias mitigation, fairness awareness and transparency to build equitable and trustworthy outcomes in the real-world implementation. In total, research done shows the possibility for advanced reinforcement learning techniques to have a significant impact to NLP, thus creating innovative NLP models capable of ensuring that they are diverse, adaptable and have necessary measures of ethics as the needs are changing. © 2024 IEEE. |
