How Multilingual Large Language Models Could Transform Education in India
Srishti Baweja, COO & Director, E2E Networks
India boasts a vibrant tapestry of 22 official languages and numerous regional dialects. However, a major hurdle exists in higher education. English dominates as the primary language of instruction and exams, creating an uneven playing field. This system is a disadvantage for students who excel in their native languages, particularly those from rural or underprivileged backgrounds. These talented individuals often struggle to compete with their English-speaking counterparts, hindering their success in the job market.
Fortunately, artificial intelligence (AI) offers a breakthrough. Why LLMs Need to Be Mindful of the Structure of Indian Languages vis a vis English. Here's a breakdown of the structural differences between Indian languages and English, highlighting why LLMs need to consider these distinctions:
Sentence Structure:
The English language primarily relies on word order to convey meaning (Subject-Verb-Object) whereas many Indian languages (Hindi, Tamil, etc.) are inflectional and use prefixes, suffixes, and word endings to indicate grammatical functions and relationships between words. This allows for more flexible word order compared to English.
Verb Conjugation:
In the English language verbs are conjugated based on tense (past, present, future) and person (singular, plural) whereas in Indian Languages Verb conjugations often incorporate additional information like respect level and gender, making them more complex.
Script and Phonetics:
The English language uses the Latin alphabet with a relatively straightforward pronunciation system whereas Indian Languages employ various scripts (Devanagari, Tamil Script etc.) with unique letter combinations and sound representations.
Why Large Language Models Need to Address These Differences
Accurate Translation: Failure to account for these structural variations can lead to misinterpretations and awkward phrasing during translation tasks.
Natural Language Processing: LLMs need to be able to decipher the intended meaning based on the grammatical structure and word inflections specific to Indian languages.
Effective Text Generation: When generating text in Indian languages, LLMs must follow the correct grammatical rules and sentence structures to produce natural-sounding and grammatically accurate output.
Focus on context: Understanding the cultural context associated with language use in India is crucial for accurate interpretation and generation.
Multilinguality: Many Indians are bilingual or multilingual. LLMs should be able to handle code-switching and the influence of other languages.
Open-Source Multilingual LLMs from India
The good news is that exciting progress is being made in India. Several universities, research groups, and businesses are creating open-source multilingual AI language models specifically tailored for the Indian context. These include:
Krutrim: This series of multilingual AI models is known for its capabilities in understanding and producing outputs in 22 Indian languages. It features a base model trained on two trillion tokens and a more advanced model, Krutrim Pro, tailored for complex problem-solving.
OpenHathi: Introduced by Sarvam AI, OpenHathi-Hi-v0.1 is the first Hindi language model in the OpenHathi series. It uses Meta AI's Llama2-7B architecture and aims to deliver GPT-3.5-like performance specifically for Indic languages.
BharatGPT: BharatGPT supports over 12 Indian languages in collaboration with Bhashini. It facilitates the rapid generation of multilingual virtual assistants with text and voice capabilities.
Bhashini: This project, led by the Government of India, goes beyond developing LLMs and aims to democratize internet and digital services access across various Indian languages. It is a comprehensive initiative focusing on breaking down language barriers and includes ULCA (Universal Language Contribution API), the largest repository of datasets for Indian languages developed by the Ministry of Electronics and Information Technology (MeitY).
Project Indus: This project focuses on building a pure Hindi Large Language Model. The project aims to include 539 million parameters and a vast collection of 10 billion tokens from Hindi and its dialects. It plans to eventually incorporate additional Indian languages and dialects.
Tamil-LLAMA: Tamil-LLAMA is a powerful LLM specifically designed for Tamil. This model tackles Tamil language tasks like summarizing text, understanding emotions in writing, and categorizing text effectively.
How Can Multilingual LLMs Transform Education?
Multilingual LLMs have the potential to revolutionize education in India by addressing several key challenges and creating new opportunities:
Bridging the Language Gap: LLMs can translate educational materials (textbooks, lectures, assignments) into various Indian languages, making them accessible to students who are not proficient in English. This promotes inclusive learning and empowers students from diverse linguistic backgrounds.
Tailored Learning Paths: LLMs can analyze student performance and learning styles, recommending suitable learning resources and adapting the pace and difficulty of the curriculum based on individual needs. This personalized approach can improve student engagement and learning outcomes.
Interactive Learning: LLMs can be integrated into educational tools and platforms to create interactive learning experiences. This could involve simulations, question-answering systems, and personalized feedback mechanisms, making learning more engaging and effective.
Support teachers through Automated Tasks: LLMs can handle administrative tasks like grading essays, providing feedback on written work, and generating personalized reports, freeing up teachers' time to focus on more individualized instruction and student interaction.
Localized Educational Material: LLMs can assist in developing educational content specifically tailored to different regions and languages. This can include incorporating local contexts, examples, and cultural references, making learning more relatable and relevant for students.
Here are some specific examples:
Imagine a student in rural Tamil Nadu who struggles with English but excels in their native language. An LLM-powered platform could translate study materials and assessments into Tamil, allowing them to grasp concepts effectively.
An LLM-based tutor could identify a student's weaknesses in a particular subject and recommend personalized learning resources or practice exercises in their preferred language.
Challenges and Considerations
Data Bias: LLMs trained on biased data can perpetuate existing inequalities. Careful data curation and monitoring are crucial to ensure fair and inclusive language models.
Teacher Training: Educators need to be equipped with the necessary skills and knowledge to integrate LLMs effectively into their teaching practices.
Infrastructure and Access: Bridging the digital divide is essential to ensure that all students have access to LLM-powered educational resources.
Overall, multilingual LLMs offer a powerful tool to create a more equitable, accessible, and engaging learning environment for students across India's diverse landscape. I see Tier-2 and Tier-3 towns being the most empowered, with AI models gradually closing the gap in educational resources and enriching the learning experience.
Srishti Baweja, COO & Director, E2E Networks
Srishti has vast experience in handling finance, legal, compliance, human resources, operations and strategy. She has helped establish E2E as one of the most powerful and trusted service providers in the domain of cloud computing.
Fortunately, artificial intelligence (AI) offers a breakthrough. Why LLMs Need to Be Mindful of the Structure of Indian Languages vis a vis English. Here's a breakdown of the structural differences between Indian languages and English, highlighting why LLMs need to consider these distinctions:
Sentence Structure:
The English language primarily relies on word order to convey meaning (Subject-Verb-Object) whereas many Indian languages (Hindi, Tamil, etc.) are inflectional and use prefixes, suffixes, and word endings to indicate grammatical functions and relationships between words. This allows for more flexible word order compared to English.
Verb Conjugation:
In the English language verbs are conjugated based on tense (past, present, future) and person (singular, plural) whereas in Indian Languages Verb conjugations often incorporate additional information like respect level and gender, making them more complex.
Script and Phonetics:
The English language uses the Latin alphabet with a relatively straightforward pronunciation system whereas Indian Languages employ various scripts (Devanagari, Tamil Script etc.) with unique letter combinations and sound representations.
Why Large Language Models Need to Address These Differences
Accurate Translation: Failure to account for these structural variations can lead to misinterpretations and awkward phrasing during translation tasks.
Natural Language Processing: LLMs need to be able to decipher the intended meaning based on the grammatical structure and word inflections specific to Indian languages.
Effective Text Generation: When generating text in Indian languages, LLMs must follow the correct grammatical rules and sentence structures to produce natural-sounding and grammatically accurate output.
Focus on context: Understanding the cultural context associated with language use in India is crucial for accurate interpretation and generation.
Multilinguality: Many Indians are bilingual or multilingual. LLMs should be able to handle code-switching and the influence of other languages.
Open-Source Multilingual LLMs from India
The good news is that exciting progress is being made in India. Several universities, research groups, and businesses are creating open-source multilingual AI language models specifically tailored for the Indian context. These include:
Krutrim: This series of multilingual AI models is known for its capabilities in understanding and producing outputs in 22 Indian languages. It features a base model trained on two trillion tokens and a more advanced model, Krutrim Pro, tailored for complex problem-solving.
OpenHathi: Introduced by Sarvam AI, OpenHathi-Hi-v0.1 is the first Hindi language model in the OpenHathi series. It uses Meta AI's Llama2-7B architecture and aims to deliver GPT-3.5-like performance specifically for Indic languages.
BharatGPT: BharatGPT supports over 12 Indian languages in collaboration with Bhashini. It facilitates the rapid generation of multilingual virtual assistants with text and voice capabilities.
Bhashini: This project, led by the Government of India, goes beyond developing LLMs and aims to democratize internet and digital services access across various Indian languages. It is a comprehensive initiative focusing on breaking down language barriers and includes ULCA (Universal Language Contribution API), the largest repository of datasets for Indian languages developed by the Ministry of Electronics and Information Technology (MeitY).
Project Indus: This project focuses on building a pure Hindi Large Language Model. The project aims to include 539 million parameters and a vast collection of 10 billion tokens from Hindi and its dialects. It plans to eventually incorporate additional Indian languages and dialects.
Tamil-LLAMA: Tamil-LLAMA is a powerful LLM specifically designed for Tamil. This model tackles Tamil language tasks like summarizing text, understanding emotions in writing, and categorizing text effectively.
How Can Multilingual LLMs Transform Education?
Multilingual LLMs have the potential to revolutionize education in India by addressing several key challenges and creating new opportunities:
Bridging the Language Gap: LLMs can translate educational materials (textbooks, lectures, assignments) into various Indian languages, making them accessible to students who are not proficient in English. This promotes inclusive learning and empowers students from diverse linguistic backgrounds.
Tailored Learning Paths: LLMs can analyze student performance and learning styles, recommending suitable learning resources and adapting the pace and difficulty of the curriculum based on individual needs. This personalized approach can improve student engagement and learning outcomes.
Interactive Learning: LLMs can be integrated into educational tools and platforms to create interactive learning experiences. This could involve simulations, question-answering systems, and personalized feedback mechanisms, making learning more engaging and effective.
Support teachers through Automated Tasks: LLMs can handle administrative tasks like grading essays, providing feedback on written work, and generating personalized reports, freeing up teachers' time to focus on more individualized instruction and student interaction.
Localized Educational Material: LLMs can assist in developing educational content specifically tailored to different regions and languages. This can include incorporating local contexts, examples, and cultural references, making learning more relatable and relevant for students.
Here are some specific examples:
Imagine a student in rural Tamil Nadu who struggles with English but excels in their native language. An LLM-powered platform could translate study materials and assessments into Tamil, allowing them to grasp concepts effectively.
An LLM-based tutor could identify a student's weaknesses in a particular subject and recommend personalized learning resources or practice exercises in their preferred language.
Challenges and Considerations
Data Bias: LLMs trained on biased data can perpetuate existing inequalities. Careful data curation and monitoring are crucial to ensure fair and inclusive language models.
Teacher Training: Educators need to be equipped with the necessary skills and knowledge to integrate LLMs effectively into their teaching practices.
Infrastructure and Access: Bridging the digital divide is essential to ensure that all students have access to LLM-powered educational resources.
Overall, multilingual LLMs offer a powerful tool to create a more equitable, accessible, and engaging learning environment for students across India's diverse landscape. I see Tier-2 and Tier-3 towns being the most empowered, with AI models gradually closing the gap in educational resources and enriching the learning experience.
Srishti Baweja, COO & Director, E2E Networks
Srishti has vast experience in handling finance, legal, compliance, human resources, operations and strategy. She has helped establish E2E as one of the most powerful and trusted service providers in the domain of cloud computing.
