Over the past 6 months, there has been a massive AI boom, with ChatGPT and other AIs making headlines. But the key driving force behind all these advancements is the incredible LLM technology. Here's a short thread about it.
A large language model is a trained DL model that understands and generates text in a human-like fashion.
As the name, it is a Large, General-Purpose Language Model that can be Pre-Trained and then Fine-Tuned for specific needs.
Pre-Trained LLMs are trained to solve common problems like text classification, question answering, doc summarization etc.
Whereas Fine-Tuned Models are tailored for a specific task like sentiment analysis in product reviews, predicting stock prices based on financial news etc.
Benefits of LLM
- A single model can be used for different tasks
- The fine-tuning process requires minimal field data
- The performance is continuously growing when you add more data and parameters
- Low Code/No Code
- Contextual Understanding
For LLM Development, ML expertise, Training examples, and training a model are not needed. All we need to think about is the Prompt Design. This is not the case in Traditional ML Development.
Crafting the perfect prompt is an art! It's all about asking the right questions, guiding AI models, & getting accurate, relevant results. Whether it's for language translation, code generation, or creative writing, nailing the prompt is the key to unlocking AI's full potential!
There are three kinds of LLM, based on prompting.
Generic - Predict the next word based on the language in the training data
Instruction Tuned - Trained to predict a response based on the given instruction
Dialog Tuned - Trained to have a dialogue by predicting the next response
Check out this video on Introduction to large language models by @googlecloud
youtu.be/zizonToFXDs
LLMs are changing the game in NLP and we're witnessing a new era of AI-driven possibilities!