byteSizedLLM@NLU of Devanagari Script Languages 2025: Language Identification Using Customized Attention BiLSTM and XLM-RoBERTa base Embeddings

AbstractThis study explores the challenges of natural language understanding (NLU) in multilingual contexts, focusing on Devanagari-scripted languages such as Nepali, Marathi, Sanskrit, Bhojpuri, and Hindi. Language identification within these languages is complex due to their structural and lexical similarities. We present a hybrid Attention BiLSTM-XLM-RoBERTa model, achieving a state-of-the-art F1 score of 0.9974 on the test set, despite limited resources. Our model effectively distinguishes between closely related Devanagari-scripted languages, providing a solid foundation for context-aware NLU systems that enhance language-specific processing and promote inclusive digital interactions across diverse linguistic communities.