Dear Golam Mahadi ,

Improving Generative AI for low-resource languages requires strategies to handle limited data. Data augmentation techniques like back-translation and paraphrasing can help increase the available text. Transfer learning allows AI models to learn from high-resource languages and adapt to smaller datasets. Multilingual embeddings, such as mBERT and XLM-R, help connect languages by sharing representations. Synthetic data generation using AI can also create diverse and useful training samples. By combining these methods, AI can generate better text, improve translations, and enhance conversational abilities for underrepresented languages.

Dear Golam Mahadi ,

Generative AI and large-scale language models (LLM) have emerged as powerful tools in the world of language preservation, especially for near-native languages. With the increasing reliance on technology for communication, learning, and cultural writing, new opportunities have emerged to mitigate the dramatic decline of languages around the world. This paper examines the role of generative AIs and LLMs in preserving endangered languages, highlighting the risks and challenges associated with their use. The study examines the underlying technologies driving these models, including natural language processing (NLP) and deep learning, and examines several cases where these technologies have been applied to low-level languages. The paper also discusses the ethics and limitations of these technologies, suggesting solutions to overcome these challenges to enhance AI’s language acquisition capabilities.

https://arxiv.org/html/2501.11496

Regards,

Shafagat

One practical strategy we’ve found effective is blending synthetic data generation with domain-specific pretraining. In my recent work on crop disease detection using AI image recognition, we faced similar low-resource challenges — not in language, but in underrepresented agricultural domains. The parallel applies: just as millet farming data is scarce, so is annotated text in low-resource dialects.

For languages, this means:

• Multimodal alignment: Leverage image–text pairs (e.g., rural signage, social media posts) to learn language cues grounded in visual context.
• Few-shot tuning: Using large pre-trained multilingual models (like mBART or XLM-R) and fine-tuning them on a handful of examples from the target dialect.
• Community-contributed corpora: Similar to how we gathered field-level annotations from local farmers, linguistic data can be crowdsourced — even if noisy — and improved via self-training loops.

This mirrors our approach in regions like Bundelkhand where formal datasets are limited but real-world patterns are rich. Generative AI in low-resource language settings can benefit from cross-domain transfer — vision, speech, even geospatial metadata — not just text.

My Paper on Advanced Crop Recommendation System :-

Article ADVANCED CROP RECOMMENDATION SYSTEM: LEVERAGING DEEP LEARNIN...

In my opinion, the most effective approach for optimizing Generative AI in low-resource languages and dialects is data synthesis, followed by human validation and refinement. This method allows us to rapidly expand the training corpus, even in the absence of extensive native datasets, while maintaining quality through expert oversight.

I’m currently applying this strategy to develop a dataset for Neapolitan dialect, one of the Southern Italian dialects, and we’re already observing promising results. By generating synthetic examples using multilingual models fine-tuned on related linguistic structures, and then carefully reviewing and correcting them with native speakers or trained linguists, we’re able to build a corpus that reflects both the richness and specificity of the dialect.

This hybrid human-AI process not only accelerates data collection but also helps preserve linguistic nuances that pure statistical models might miss. In this way, we empower Generative AI to support underrepresented languages with both scalability and fidelity.