The rapid advancements in Large Language Models (LLMs) like OpenAI (ChatGPT) and Llama have sparked a global AI race, with countries vying to develop their own domestic AI capabilities.

A country like India, with unique linguistic diversity and growing digital economy, is no exception. Thus arises the ever-pertinent question - does India truly need its own LLM, and if yes, what are the potential implications?

Potential Benefits of an India-Specific LLM

There are several compelling reasons why India may benefit from developing its own LLM spanning vast spheres of technology, language, politics, economics, and of course, society. These potential benefits include linguistic nuance, specific design for Indian use cases, technological sovereignty, and economic opportunities.

Linguistic Nuance

India has 22 scheduled languages and countless dialects. An India-specific LLM could better capture the nuances of Indian languages, culture, and context compared to globally focused models, which tend to capture more western sentiments and contexts.

Tailored for Indian Use Cases:

An Indian LLM could be optimized for specific challenges and use cases relevant to the Indian market, such as handling code-switching (mixing two or more languages in a conversation – for example, Hinglish), understanding colloquialisms, and providing localized information.

Technological Sovereignty:

Developing domestic AI capabilities could help strengthen India's technological sovereignty and reduce reliance on foreign models, which may have biases or limitations when applied to the Indian context. This is especially important considering certain biased and unfounded negative connotations with respect to India that might be transferred to AI models.

Economic Opportunities:

The development of an Indian LLM could create new opportunities such as employment, while opening the Indian market to the development of AI-powered applications and specifically tailored services.

Potential Drawbacks and Considerations

While the potential benefits are compelling, there are also some potential drawbacks and crucial factors to consider, which ought to influence the decision to develop such an LLM.

Existing Models May Suffice:

Broad, multilingual global LLMs may already be able to reason around cultural differences and serve the Indian market adequately, reducing the need for a resolute Indian model and making it redundant.

Resource Constraints:

Building a high-quality LLM from scratch requires significant investment in data, computational power, and research talent. It may not be the most efficient use of resources compared to leveraging existing models to develop localized AI applications.

Ethical Concerns: There are concerns about the potential misuse of powerful AI models, such as the spread of misinformation or the perpetuation of biases. Careful governance and ethical frameworks would be critical for an India-specific LLM.

Interoperability:

If India develops its own LLM, it will need to ensure that it can seamlessly integrate with global AI systems and standards, to avoid creating a siloed ecosystem.

Recent Developments

While there are several considerations of developing an LLM, including those beyond the scope of this article, developments are already underway in India, and many are reaching reasonable maturity. For example, BharatGPT is an indigenous large language model (LLM) being developed by corover.ai, which is an Indian conversational AI company, and other collaborators. BharatGPT is being designed with a focus on Indian languages and use cases. At the time of writing, it supports several over 12 Indian languages for text and 14 languages for voice interactions. It also supports over 120 languages globally.

There are also other companies such as Tech Mahindra, Reliance Industries, and Ola who have announced their plans to develop India specific LLMs and specialized AI applications.

Recommendations

Based on the potential benefits, drawbacks and other considerations, a balanced approach may be warranted. India could explore ways to:

Collaborate with Global LLM Providers:

India could work with global LLM providers to customize and localize existing models for the Indian context, leveraging their expertise and resources. This will help accelerate the development of viable use cases while preserving vital resources.

Invest in Research and talent:

India could invest in developing talent, which can develop localized language models. With the speed of development in Generative AI space, it is important to have local skills. Further, the investments can be made to improve the cultural competence and performance of universally applicable LLMs in Indian languages and use cases.

Focus on Specialized AI Applications:

India could develop specialized AI models and applications tailored for India, while leveraging the capabilities of large, pre-existing, general-purpose LLMs.

Establish Robust Governance Frameworks:

India should establish robust governance frameworks to ensure the ethical and responsible development of AI, with a focus on data privacy, algorithmic fairness, and transparency.

Conclusion

The decision to develop an India-specific LLM is a complex one, with valid arguments on both sides. There is merit in developing talent, which can develop localized foundational models in India. However, a balanced approach must be kept in mind to optimize resources and harness the power of available technologies. By taking a calculated and collaborative approach, India can leverage the power of AI to drive its economic and social development while ensuring that the benefits are equitably distributed across the country.

Self authored