# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 9 Future Directions and Development Trends
### Different Languages and Domains  
The current LLM research and development are primarily confined to the English language. According to Ethnologue, there are 7168 living languages in the world. A language becomes endangered when its users begin to teach and speak a more dominant language to their children than their native language. Over 3045 languages are endangered today. LLMs can play a pivotal role in preserving and promoting all world languages. Low-resource languages need more curated and annotated datasets in machine-readable format to train LLMs. Also, some languages are spoken only without written counterparts. For such cases, speech-to-text transcription is required. To ensure linguistic inclusivity, researchers are investigating the following strategies. Data augmentation and synthesis techniques are investigated to create synthetic data to enlarge the training datasets. Some techniques include back-translation, paraphrasing, and data generation through linguistic rules. Another approach to deal with low linguistic resources is to leverage transfer learning. This involves pretraining models on high-resource languages (e.g., English) and transferring knowledge to low-resource languages. As multilingual LLMs share model parameters across languages, this helps in improving performance for low-resource languages. Also, developing models capable of zero-shot or few-shot learning using high-resource languages enables them to perform tasks in low-resource languages with minimal or no annotated data. For example, methods such as meta-learning and cross-lingual transfer target this goal. However, the effectiveness of such methods remains to be seen. Semi-supervised and self-supervised learning approaches can be leveraged for labeled and unlabeled data for model training in low-resource contexts. Unlabeled data can be effectively utilized using techniques such as self-training or pseudo-labeling. Another approach to help low-resource situations is to design language-specific architectures and models that are tailored to the linguistic characteristics of low-resource languages. Adapting LLMs to specific linguistic features and morphological structures improves their effectiveness. Community involvement in building datasets for low-resource languages through collaboration and crowdsourcing is vital. Resource sharing and knowledge transfer between linguistic communities in the form of datasets, linguistic tools, and methodologies will immensely help low-resource languages. Once LLMs are developed for low-resource languages, they can aid in preserving and promoting them. For example, LLMs can help in documentation, translation, education, and cultural preservation. LLMs can be leveraged to document low-resource and endangered languages by analyzing written texts and transcribing spoken language. LLMs will also enable the creation of digital archives, cataloging historical texts, and documenting stories and folklore in native languages. More importantly, LLMs can be used to support indigenous communities by providing tools that assist in preserving their languages and traditions. These activities help to preserve linguistic heritage that might otherwise be lost. LLMs can translate between high-resource and low-resource languages, making the information more accessible and fostering communication across linguistic barriers. Also, LLMs can be used to support language revitalization efforts by providing language learning resources and generating teaching materials. Furthermore, LLMs will aid in developing language-learning applications for low-resource and endangered languages. LLMs will provide language researchers with advanced tools and resources for linguistic analysis, corpus creation, and comparative studies on a scale that was infeasible before. Furthermore, LLMs will foster collaborative language preservation by facilitating collective work and communication across language barriers.