This is a technology-driven era where AI is ruling organizations across the world. AI is a technology that everyone should be familiar with regardless of their occupation. For the last four years, we have been hearing about advancements in this field, such as Generative AI aiding in research, medical, financial, and marketing domains.
One such advancement is the introduction of large language models (LLMs), which are intelligent machines that understand and generate human-like texts. LLMs are essentially neural networks that are fed with a large corpus of text(from across the web, books and journals, news articles, and so on) and generate text outputs.
Large Language Models (LLMs) are advanced neural networks trained on vast text datasets to understand and generate human-like text. These models are instrumental in various applications including chatbots, content generation, and language translation, leveraging techniques like self-attention and reinforcement learning for enhanced performance
We introduce LLMs in this post and discuss their history, applications, and more. Stay tuned!
What Are LLMs?
I’m sure you have used or heard of ChatGPT, which answers users’ questions, summarizes texts, and generates code based on the given prompt. ChatGPT is a large language model trained on a large corpus of data to understand users’ prompts and generate text output accordingly.
LLMs are a breakthrough in Natural Language Processing(NLP) and linguistics, enhancing how machines understand and generate human language. The word Large in large language models corresponds to the massive corpus they are trained on and sometimes refers to the huge set of parameters they comprise.
LLMs have revolutionized human-machine interactions and are widely used to develop conversational or chatbots, content generation, summarization, and translation.
Recommended Read: Building a Chatbot using Python
Popular examples of large language models are the GPT series, Gemini, Claude, Llama, etc.
So, How Do Large Language Models Work?
As mentioned, large language models are artificial neural networks that leverage deep learning techniques to understand large textual datasets and generate responses.
One key mechanism enabling these language models to understand human language is the attention mechanism. The attention mechanism was formally introduced in the paper Attention is All You Need and is a layer added to deep learning models like transformers, enabling these models to focus on specific parts of the input.
Self-attention mechanism is an advanced technique that enables the language models to understand the context or relationship between words in an input. Here’s an example: consider the text:
“Santorini is an island in Greece, famous for its blue color dome-shaped buildings.” The self-attention mechanism allows language models to map the related words, just like we do – Santorini-> dome-shaped buildings.
There are primarily three steps in working on the language models :
Training: The Large Language Models are fed with a huge amount of data such as news articles, books, blogs, and other textual information to make them accustomed to the language and wordsFine-tuning: The model is exposed to a comparatively small and task-specific dataset to improve its performancePrompt Engineering: Another technique to improve the performance of language models is prompt engineering, which focuses on how we interact with the llm agent. More detailed prompts result in a better response
RLHF for Language Models
Reinforcement Learning is the key mechanism in building smart machines that can make decisions based on feedback. RLHF stands for Reinforcement Learning from human feedback. It is a method used to fine-tune LLMs using human feedback.
The cognitive power and understanding of humans come into the picture here. Humans interact with the language model, document the response, and provide feedback to the agent. This feedback can be in the form of rankings.
This feedback is used to train a reward model, which learns which responses the user prefers. This human intervention aids in the language model’s better performance and the responses are aligned with human values.
Here’s a flowchart that helps us understand RLHF for language models.
Uses of LLMs
LLMs are used for content creation, text summarization, translation, and other linguistic tasks. LLMs are leveraged in healthcare, education, marketing, finance, social media, and advertising.
The main form of LMS in companies is interactive chatbots, which interact and engage with customers, resolve their queries, and provide information.
Social media and content creation and generation are used to create unique content for networking platforms, generate ideas, and plan.
LLMs can be used to create targeted curricula for students, focusing on their development and training.
Conclusion
This is just the start of an exciting learning experience in large language models! In the coming posts, we will understand how to run large language models on our laptops using APIs.
Useful Resources
If you want to learn and experiment with large language models, you can follow some popular courses and blogs. Below are some of them: