What is a Language Model?
Paraphrased from ChatGPT answers -
ELI5 (Explain like I'm 5) version ^-
A model is like a computer program that has been trained to do a specific task. Just like how we humans learn from experience, AI models learn from data.
Think of it like a game of "guess who". You have a bunch of pictures of people, and you want to guess who someone is based on some clues. An AI model can be trained to do this too! You give it lots of pictures of people and tell it who each person is. Then you give it some clues, like "has brown hair" or "wears glasses", and it tries to guess who the person is based on those clues.
AI models can be very useful for solving problems and making predictions. But, they're not perfect and need to be trained carefully to avoid mistakes.
You need an algorithm to create AI models.
Algorithms are like a set of instructions that tell the AI model what to do. Think of it like a recipe for baking cookies - you need to follow the recipe step by step to make the cookies turn out just right.
When creating AI models, the algorithm is like the recipe. It tells the AI model what kind of data to look for, how to process that data, and how to make predictions based on the data.
Just like how there are different recipes for making different kinds of cookies, there are different algorithms for creating different kinds of AI models. Some algorithms work better for certain types of data, and some algorithms work better for certain kinds of predictions.
For example, if you want to train an AI model to recognize pictures of cats and dogs, you might use an algorithm called "convolutional neural networks" (CNNs). This algorithm is good at recognizing patterns in images, so it can tell the difference between a cat and a dog.
A language model is like a very smart computer program that can understand and use human language. A language model is trained on lots of text to learn how language works.
The significance of a language model is that it can help us communicate with computers more easily. Instead of having to type out every single command or question, we can talk to computers like we talk to humans.
Imagine you have a robot that can draw pictures of animals. The robot has some settings that you can change to make it draw different kinds of animals. For example, you can change the size of the picture, the color of the lines, and the shape of the animal.
These settings are like parameters in a language model.
A language model has many different parameters that you can adjust to make it work better for different tasks. Some examples of parameters in a language model might include:
- Word embeddings: These are like codes that represent each word in the language model's vocabulary. By adjusting the parameters of the word embeddings, the language model can learn to better understand the meanings of words.
- Hidden layers: These are like filters that the language model uses to process the input text. By adjusting the parameters of the hidden layers, the language model can learn to better recognize patterns in the text.
- Learning rate: This parameter controls how quickly the language model updates its parameters based on the training data. By adjusting the learning rate, the language model can learn more quickly or more slowly, depending on the complexity of the task.
The number of parameters in a language model tells us how smart and complex it is. Think of it like a toy robot that can do different things. If the robot has more buttons and features, it can do more things and is more complex.
So, a language model with more parameters can understand and use language better than a smaller one. But it also needs more time and resources to learn and work.
 |
| Source: AI for Everyone |
Some examples of large language models (LLMs) that use very high numbers of parameters are OpenAI's GPT-3 with 175 billion parameters, Google's Pathways Language Model (PaLM) with 540 billion parameters, and Deep Learning Recommender System from Facebook with 12 trillion parameters.
They have lots of buttons and features, so they can do lots of different language tasks like talking, translating, or writing stories. But how well they work depends on what we ask them to do, and how they were made.
One of the most important parts of a LLM is a deep neural network. It's like a big maze made up of many small parts, called "neurons". These neurons work together to figure out what words mean and how to use them correctly.
When you give a LLM some words to read or ask it a question, the words go into the neural network like a puzzle. Each neuron in the network works to figure out a small part of the puzzle. Then, all the neurons work together to put the puzzle together and give you an answer.
The more neurons a LLM has, the smarter it is and the better it can understand and use language. But, just like our brain, the LLM needs lots of practice and training to get really good at language.
Prompt engineering is like giving a language model a special job to do. Imagine you have a robot that can do lots of things, like draw pictures or cook food. But, if you want the robot to do a specific job, like making a sandwich, you have to give it instructions on how to do that job.
Similarly, when we do prompt engineering with a language model, we're giving the language model a specific job to do, like answering questions about history. We do this by giving the language model a prompt, which is like a question or a statement that tells the language model what we want it to do.
By giving the language model a prompt that's related to the task we want it to do, we're helping it focus on the right information and generate more relevant responses.
--
The significance of the number of parameters depends on the specific task and context in which the language model is being used. For example, a smaller language model may be sufficient for simpler tasks such as sentiment analysis, while a larger language model (LLM) may be necessary for more complex tasks such as machine translation or question-answering.
These large language models (LLMs) consist of deep neural networks with billions of trainable parameters, trained on massive datasets of unlabelled text, and have demonstrated impressive results on a wide variety of natural language processing (NLP) tasks.
Large language models can be classified into -
- base LLMs
- instruction-tuned LLMs.
The base LLMs are the foundation models trained on massive datasets available in the public domain. Out of the box, these models are good at word completion. They can predict what comes next in the sentence. Examples of base LLMs include OpenAI’s GPT 3.5 and Meta’s LLaMa. When you pass a string as input to the base model, it generates another string that typically follows the input string.
The instruction-tuned LLMs are fine-tuned variations of the foundation model designed to follow instructions and generate an appropriate output. The instructions are typically in a format that describes a task or asks a question. OpenAI’s gpt-3.5-turbo, Stanford’s Alpaca, and Databricks’ Dolly are some of the examples of instruction-based LLMs. The gpt-3.5-turbo model is based on the GPT-3 foundation model, while Alpaca and Dolly are fine-tuned variations of LLaMa.
Generative Artificial Intelligence - Any type of artificial intelligence (Al) that can be used to create new text, images, video, audio, code or synthetic data.
^ ELI5 or Explain like I'm 5 is a term used on the Reddit forum to ask for a simple explanation to a complex topic.
References -
ChatGPT
ChatGPT and Generative AI: The Big Picture
Related: Prompt Engineering - Resource Links
Comments
Post a Comment