Why is it in the news?

• Hanooman represents a series of Large Language Models (LLMs) developed by the BharatGPT Group, spearheaded by IIT Bombay, with the primary objective of providing conversational AI services similar to ChatGPT.

More about the news

• Hanooman boasts the capability to respond effectively in a variety of Indian languages, including Hindi, Tamil, and Marathi, catering to a diverse linguistic landscape in the Indian context.
• The ambit of Hanooman’s application is broad, with a focus on four key domains: healthcare, governance, financial services, and education. It aims to leverage its language processing capabilities to facilitate interactions and services in these sectors.

About LLMs

• LLMs, including Hanooman, harness deep learning methodologies to process language comprehensively. By training on massive datasets, they excel in tasks involving natural language understanding and generation.
• Hanooman, like other LLMs, relies on extensive training datasets, often including sources such as Wikipedia and OpenWebText.

 Applications of LLMs

• Medical: Hanooman can contribute to tasks like protein structure prediction, aiding in disease pattern identification and outcome prediction.
• Retail: In the retail sector, dynamic chatbots powered by Hanooman can enhance customer experiences by providing personalized assistance and recommendations.
• Software: LLMs like Hanooman are instrumental in writing software code and instructing robots on various physical tasks, contributing to automation and efficiency in various industries.
• Finance: Hanooman’s capabilities extend to summarizing earnings calls and transcribing crucial financial meetings, streamlining communication and decision-making processes in the finance sector.
• Marketing: Organizations can utilize Hanooman to organize and analyze customer feedback, enabling better understanding of consumer sentiments and preferences.

Challenges in Developing LLMs

• Capital Investment: Developing and maintaining LLMs requires significant financial resources due to the high computational costs involved in training and infrastructure maintenance.
• Large Datasets: High-quality, extensive datasets are essential for effective training of LLMs, posing a challenge, especially in languages with limited available data.
• Technical Expertise: Advanced knowledge in deep learning, natural language processing, and related fields is necessary for the successful development and optimization of LLMs.
• Computing Infrastructure: Robust computing infrastructure is imperative to support the computational demands of LLM training and deployment.
• One of the primary challenges in developing LLMs for Indian languages is the scarcity of high-quality training datasets.

Generative Pretrained Transformer (GPT) vs LLM

• Generative Pretrained Transformer (GPT) is a specific type of LLM renowned for its ability to generate human-like text through deep learning techniques.
• GPTs can generate new text based on the input they receive, mimicking human-like language generation.
• Before fine-tuning for specific tasks, GPTs are pretrained on large corpora of text data, enabling them to grasp the nuances of natural language.
• GPTs utilize transformer-based neural network architectures, renowned for their effectiveness in processing sequential data, particularly in language-related tasks.