Challenges in building accurate language models for diverse linguistic contexts

Imagine opening an app designed to help you manage your small business online only to discover every menu, option, and instruction is in a language you don't fully understand. You're attempting to list products, assign prices and complete transactions, and you don't quite know what to do. The instructions are confusing and translation tools only partially help.

Frustration builds, and after a period you simply abandon the digital platform, limiting yourself to local, in-person customers. This is the reality for millions of people every day when digital services are not accessible in their own languages. KPMG-Google study found that 60% of Indian-language internet users mentioned limited language support as their biggest obstacle to adoption. 

Today, India is celebrated for being the third most digitized economy in the world with close to a billion active internet users. But as these numbers rise, language exclusion is one of the major barriers to our digital progress.

More than 90% of Indians do not speak English as their first language, yet mostly all digital services, from banking apps to edtech platforms, from healthcare portals to retail e-commerce, still default to English as the primary medium. That misalignment acts as a barrier, depriving millions of meaningful digital access, and it highlights a pressing need of building language models that can speak India’s 22 scheduled languages, thousands of dialects, and the lived linguistic context of its people.

The technical mountain: what makes language modeling so tough in India?

High-quality, large-scale datasets, the lifeblood of modern large language models (LLMs), are severely lacking for Indian languages. Many languages, especially low-resource ones, have no substantial parallel corpora or benchmarks. Furthermore, Indian languages have a range of scripts, Devanagari, Tamil, Gurmukhi, and so on. Each script has unique characteristics. For example, complex conjuncts and inconsistent spacing make tokenization and encoding difficult. The grammatical richness, freedom of word order, and script-specific challenges add another layer of complexity to training of language models.

In India, even in a single sentence, one commonly sees code transitions like Hinglish (“आप movie देख रहे हो?”) which have combined English words with a local grammar. Annotating and training models to understand that fluidity requires sophisticated, context-aware tagging beyond rigid monolingual approaches. According to a study, over 60% of Indian internet users regularly use Hinglish. For language models, such hybrid text breaks the conventional boundaries of training data.

Cultural nuance and contextual fluency 

Understanding language goes beyond simple word-to-word literal translation, it needs cultural context and idiomatic awareness. Off-the-shelf multilingual models may lack these subtleties, leading to translations that sound technically correct but culturally tone-deaf or bland. Most of the LLMs available right now are pre-trained in English and other globally-dominant languages. This skews model competency and introduces biases, such as skewed cultural norms or misrepresentation of minority language content, impacting fairness and inclusivity.

The way forward

Developing accurate language models for differentiating linguistic context needs a combination of innovation in technology and collaborative ecosystems. No one organization can ultimately address the challenge of language diversity in India, this requires collaboration of government, academia, industry, and communities.

Second, an accurate model for various linguistic contexts must be hybrid where neural architectures are combined with rule-based methods. While deep learning does wonders for natural language processing, languages in India are often complicated by structure, often contain agglutination, and inherently imply semantic meaning based on context. By leveraging linguistic rules, morphology analyzers, and contextual embeddings working together, we could significantly leverage the accuracy of models in these contexts.

Another important facet to continuous learning stems from user corrections and input. Language is always changing; models should not be static as the vocabulary and colloquialisms change, the dynamic of language constantly. An ecosystem where the community has an active role in providing corrections and refinements to language would allow a model's understanding to improve.

Finally, there is a pressing need to make these solutions accessible and scalable for businesses. Today, most companies, whether in banking, healthcare, retail, or education, default to English because it is the only scalable option. But as multilingual models mature, the real transformation will come when every product or service can seamlessly “speak” to users in their native tongue, across every touchpoint.

By Nakul Kundra, Co-Founder, Devnagri AI