Large Language Models (LLMs)

Why in news?

• As per Principal Scientific Advisor, India will set up a “high powered committee” to explore the development of Large Language Models (LLMs), tools that harness Artificial Intelligence (AI) to create applications that can understand and process human language.

About LLMs

• Large language models are advanced artificial intelligence systems designed to understand and generate human language. They are trained on vast datasets and can perform a wide range of natural language processing tasks. These models use deep learning techniques, particularly neural networks, to process and generate human language.

Key Characteristics of Large Language Models

• Scale: Large language models are massive in terms of the number of parameters they contain. They can have tens of billions or even hundreds of billions of parameters. This scale allows them to capture intricate language patterns and context.
• Pre-training: These models are pre-trained on extensive text corpora, such as books, articles, and websites, to learn grammar, vocabulary, and world knowledge. This pre-training enables them to generate human-like text.
• Fine-Tuning: After pre-training, models can be fine-tuned on specific tasks. For instance, they can be fine-tuned for text generation, translation, summarization, question-answering, and more.

Examples of Large Language Models

• GPT-3 (Generative Pre-trained Transformer 3): Developed by OpenAI, GPT-3 is one of the most well-known large language models. It has 175 billion parameters and can generate coherent, contextually relevant text. GPT-3 can be used for various applications, from content generation to chatbots.
• BERT (Bidirectional Encoder Representations from Transformers): Developed by Google, BERT is another significant language model. It has 340 million parameters and is designed for natural language understanding. BERT has improved the performance of search engines and text classification tasks.
• T5 (Text-to-Text Transfer Transformer): T5 is a model developed by Google Research that frames all NLP tasks as a text-to-text problem. It has 11 billion parameters and is known for its flexibility in handling various language tasks, including translation, summarization, and more.

Applications of Large Language Models

• Content Generation: Large language models can create human-like text for articles, stories, or marketing copy.
• Translation: They can translate text between languages accurately.
• Question Answering: These models can provide detailed answers to questions based on a given context.
• Summarization: They can generate concise summaries of lengthy texts.
• Chatbots and Virtual Assistants: Large language models power chatbots and virtual assistants, making them more conversational and context aware.
• Sentiment Analysis: They can analyze and understand the sentiment expressed in text, making them useful for social media analysis and customer feedback.
• Language Understanding and Processing: These models are employed in search engines to better understand user queries and provide relevant results.

PRACTICE QUESTION:

How many of the following are applications of Large Language Models (LLMs)?

1. Automated video editing
2. Summarisation of lengthy texts
3. Writing short stories

Select the correct answer using the code given below:

(a) Only one

(b) Only two

(c) All the three

(d) None of these

Answer