The current positional encoding approaches, while essential for capturing word order information in language models, may have limitations when applied to morphologically diverse languages. One potential limitation is the generalization of positional encoding techniques designed for morphologically poor languages to languages with rich morphological systems. To address this limitation, improvements can be made by developing adaptive positional encoding mechanisms that can dynamically adjust based on the morphological complexity of the language. This adaptive approach could involve incorporating linguistic features related to morphology into the positional encoding process, allowing the model to better capture the nuances of word order variations in different languages. Furthermore, exploring the integration of contextual information related to morphology, such as morphosyntactic patterns and syntactic structures, into the positional encoding scheme can enhance the model's ability to encode positional information effectively in morphologically diverse languages.