Understanding LLMs,
We are hearing about AI everywhere and how rapidly it is being adopted in products like ChatGPT and now Gemini, Claude, etc. I am quite intrigued to know what all this is about and how it works, followed by some research on the internet, where I could find out some very basic things about these models and share here with you some such findings. But how do they manage to understand language so well? That’s where the next concepts come into play: parameters, architecture, and training.
What Exactly are LLMs?
Large Language Models are what LLM stands for. They are primarily AI programs trained to understand as well as generate written text. They may answer questions, write essays, summarize articles, create fables, or poems.
They belong to a larger category called foundation models, the training of which predates thousands of terabytes worth of data. While LLMs, for the most part, has text data from books, websites, and codes, their size ranges anywhere from gigabytes (GB) to petabytes (PB). As an example:
1 petabyte: 1 million gigabytes
1 gigabyte: Approximately 178 million words.
OpenAI’s GPT-3, a well-known LLM, has 175 billion parameters and was trained on datasets spanning terabytes in size.
Thus, LLMs have been built by developing appropriate models through patterns and relationships found in the texts so that they could create human-like text.
How Do LLMs Work?
LLMs get defined by three key parts:
Data: They use enormous datasets rich in text through which the model learns how to use language.
Architecture: LLMs are based on a special type of neural net called Transformer, which is very good in processing sequences like sentences and understanding their context.
Training: In training, the model predicts the next words in a sentence, and then it would change its internal parameters to optimize them, in case its prediction is wrong. This eventually helps the model improve at producing text with meaning and semantics.
Fine-Tuning for Specific Tasks
After general training, the model can be fine-tuned using domain-specific data for specialized tasks.
Applications of LLMs
Chatbots:Used in customer service, tech support, and virtual assistants.
Content Creation: Generate social media posts, blogs, and marketing content.
Software Development: Help with code suggestions, explanations, and debugging.
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