What Does Large Language Model (LLM) Mean?
A large language model (LLM) is a type of machine learning model that can perform a variety of natural language processing (NLP) tasks, including generating and classifying text, answering questions in a conversational manner and translating text from one language to another.
The label “large” refers to the number of values (parameters) the model can change autonomously as it learns. Some of the most successful LLMs have hundreds of billions of parameters.
LLMs are trained with immense amounts of data and use self-supervised learning to predict the next token in a sentence, given the surrounding context. The process is repeated over and over until the model reaches an acceptable level of accuracy.
Once an LLM has been trained, it can be fine-tuned for a wide range of NLP tasks, including:
- Building conversational chatbots like ChatGPT.
- Generating text for product descriptions, blog posts and articles.
- Answering frequently asked questions (FAQs) and routing customer inquiries to the most appropriate human.
- Analyzing customer feedback from email, social media posts and product reviews.
- Translating business content into different languages.
- Classifying and categorizing large amounts of text data for more efficient processing and analysis.
Techopedia Explains Large Language Model (LLM)
Large language models typically have a transformer-based architecture. This type of AI architecture uses self-attention mechanisms to calculate a weighted sum for an input sequence and dynamically determine which tokens in the sequence are most relevant to each other.
What Are Large Language Models Used For?
Large language models are used for few-shot and zero-shot scenarios when there is little or no domain-tailored data available to train the model.
Both few-shot and zero-shot approaches require the AI model to have good inductive bias and the ability to learn useful representations from limited (or no) data.
How Are Large Language Models Trained?
Most LLMs are pre-trained on a large, general-purpose dataset that is similar in statistical distribution to the task-specific dataset. The purpose of pre-training is for the model to learn high-level features that can be transferred to the fine-tuning stage for specific tasks.
The training process of a large language model involves:
- Pre-processing the text data to convert it into a numerical representation that can be fed into the model.
- Randomly assigning the model’s parameters.
- Feeding the numerical representation of the text data into the model.
- Using a loss function to measure the difference between the model’s outputs and the actual next word in a sentence.
- Optimizing the model’s parameters to minimize loss.
- Repeating the process until the model’s outputs reach an acceptable level of accuracy.
How Do Large Language Models Work?
A large language model uses deep neural networks to generate outputs based on patterns learned from training data.
Typically, a large language model is an implementation of a transformer architecture. Transformer architectures allow a machine learning model to identify relationships between words in a sentence — regardless of their position in the text sequence — by using self-attention mechanisms.
Unlike recurrent neural networks (RNNs) which use recurrence as the main mechanism for capturing relationships between tokens in a sequence, transformer neural networks use self-attention as their main mechanism for capturing relationships. The relationships between tokens in a sequence are calculated using attention scores that represent how import a token is in regards to the other tokens in the text sequence.
Examples of Large Language Models
Some of the most popular large language models are:
GPT-3 (Generative Pretrained Transformer 3) – developed by OpenAI.
BERT (Bidirectional Encoder Representations from Transformers) – developed by Google.
RoBERTa (Robustly Optimized BERT Approach) – developed by Facebook AI.
T5 (Text-to-Text Transfer Transformer) – developed by Google.
CTRL (Conditional Transformer Language Model) – developed by Salesforce Research.
Megatron-Turing – developed by NVIDIA
