The rapid development of neural network-based models has revolutionized the field of text generation, translation, and understanding, but significant limitations still persist, particularly regarding generalization and adaptability across diverse linguistic tasks. A novel approach introduced here leverages randomization of conceptual embeddings to address these challenges, enhancing model performance by introducing semantic variability into the training process. This method fosters the ability of the language model to generate more flexible and contextually rich outputs, with improvements noted in perplexity, accuracy, fluency, and vocabulary diversity. The results demonstrate the potential of conceptual embedding techniques to alleviate common issues such as over-reliance on learned patterns, ultimately improving robustness across a wide range of tasks. Performance comparisons between the baseline and enhanced models suggest that the conceptual embedding injection strategy not only increases the model's linguistic creativity but also promotes superior handling of unfamiliar input contexts. While computational constraints were observed, the overall improvements in linguistic quality, coupled with more dynamic internal representations, highlight the significance of embedding randomization as a valuable tool for advancing text generation models.