Artificial Intelligence - AI

What is a Large Language Model - LLM?

A Large Language Model - LLM is a Deep Learning - DL algorithm that can perform a variety of Natural Language Processing - NLP tasks. Large Language Models - LLMs use Transformer Models and are trained using massive datasets — hence, large. This enables them to recognize, translate, predict, or generate text or other content.

Large Language Models - LLMs are also referred to as Neural Networks - NNs, which are computing systems inspired by the Human brain. These Neural Networks work using a Network of Nodes that are layered, much like Neurons.

In addition to teaching Human languages to Artificial Intelligence - AI applications, Large Language Models - LLMs can also be trained to perform a variety of tasks like understanding Protein Structures, writing Software Code, and more. Like the Human brain, Large Language Models - LLMs must be Pre-Trained and then Fine-Tuned so that they can solve Text Classification, Question and Answering, Document Summarization, and Text Generation Problems. Their problem-solving capabilities can be applied to fields like Healthcare, Life Sciences, Telecommunications, Satellite Communications, Systems Engineering, Network Engineering, Data Science, Finance, Space Exploration, etc., where Large Language Models - LLMs serve a variety of NLP applications, such as translation, Chatbots, artificial Intelligence - AI assistants, etc.

Large Language Models - LLMs also have large numbers of parameters, which are akin to memories the model collects as it learns from training. Think of these parameters as the model’s knowledge bank.

What is a Transformer Model?

A Transformer Model is the most common architecture of a Large Language Model. It consists of an encoder and a decoder. A Transformer Model processes data by tokenizing the input, then simultaneously conducting mathematical equations to discover relationships between tokens. This enables the computer to see the patterns a Human would see were it given the same query.

Transformer Models work with self-attention mechanisms, which enables the model to learn more quickly than traditional models like long short-term memory models. Self-attention is what enables the Transformer Model to consider different parts of the sequence, or the entire context of a sentence, to generate predictions.

Key Components of Large Language Models - LLMs

Large Language Models are composed of multiple Neural Network layers. Recurrent layers, Feedforward layers, Embedding layers, and Attention layers work in tandem to process the input text and generate output content.

The embedding layer creates embeddings from the input text. This part of the Large Language Model - LLM captures the Semantic and Syntactic meaning of the input, so the model can understand context.

The Feedforward layer - FFN of a Large Language Model - LLM is made of up multiple fully connected layers that transform the input embeddings. In so doing, these layers enable the model to glean higher-level abstractions — that is, to understand the user's intent with the text input.

What is the difference between Large Language Models - LLMs and Generative Artificial Intelligence - AI?

Generative Artificial Intelligence - AI is an umbrella term that refers to Artificial Intelligence - AI models that have the capability to generate content. Generative AI can generate text, code, images, video, and music. Examples of Generative AI include Midjourney, DALL-E, and ChatGPT.

Large Language Models - LLMs are a type of Generative AI that are trained on text and produce textual content. ChatGPT is a popular example of Generative Text AI.

All Large Language Models - LLMs are Generative AI

The Recurrent layer interprets the words in the input text in sequence. It captures the relationship between words in a sentence.

The attention mechanism enables a language model to focus on single parts of the input text that is relevant to the task at hand. This layer allows the model to generate the most accurate outputs.

Apply transformers to your search applications

There are three main kinds of Large Language Models - LLMs:

- Generic or raw language models predict the next word based on the language in the training data. These language models perform information retrieval tasks.
- Instruction-tuned language models are trained to predict responses to the instructions given in the input. This allows them to perform sentiment analysis, or to generate text or code.
- Dialog-tuned language models are trained to have a dialog by predicting the next response. Think of Chatbots or Conversational AI.

How do Large Language Models - LLMs work?

A Large Language Model - LLM is based on a Transformer Model and works by receiving an input, encoding it, and then decoding it to produce an output prediction. But before a Large Language Model - LLM can receive text input and generate an output prediction, it requires training, so that it can fulfill general functions, and fine-tuning, which enables it to perform specific tasks.

Training: Large Language Models - LLMs are Pre-Trained using large textual datasets from sites like Wikipedia, GitHub, or others. These datasets consist of trillions of words, and their quality will affect the language model's performance. At this stage, the large language model engages in unsupervised learning, meaning it processes the datasets fed to it without specific instructions. During this process, the LLM's AI algorithm can learn the meaning of words, and of the relationships between words. It also learns to distinguish words based on context. For example, it would learn to understand whether "right" means "correct," or the opposite of "left."

Fine-tuning: In order for a Large Language Model - LLM to perform a specific task, such as translation, it must be fine-tuned to that particular activity. Fine-tuning optimizes the performance of specific tasks.

Prompt-tuning fulfills a similar function to fine-tuning, whereby it trains a model to perform a specific task through few-shot prompting, or zero-shot prompting. A prompt is an instruction given to an LLM. Few-shot prompting teaches the model to predict outputs through the use of examples. For instance, in this sentiment analysis exercise, a few-shot prompt would look like this:

Customer review: This plant is so beautiful!
Customer sentiment: positive

Customer review: This plant is so hideous!
Customer sentiment: negative

The Language Model would understand, through the semantic meaning of "hideous," and because an opposite example was provided, that the customer sentiment in the second example is "negative."

Alternatively, Zero-Shot Prompting does not use examples to teach the Language Model how to respond to inputs. Instead, it formulates the question as "The sentiment in ‘This plant is so hideous' is…." It clearly indicates which task the Language Model should perform, but does not provide problem-solving examples.

Benefits of Large Language Models - LLMs

With a Broad Range of Applications, Large Language Models - LLMs are exceptionally beneficial for problem-solving since they provide information in a clear, conversational style that is easy for users to understand.

Large Set of Applications: They can be used for language translation, sentence completion, sentiment analysis, question answering, mathematical equations, and more.

Always Improving: Large Language Model - LLM performance is continually improving because it grows when more data and parameters are added. In other words, the more it learns, the better it gets. What’s more, Large Language Models - LLMs can exhibit what is called "in-context learning." Once an LLM has been Pre-Trained, Few-Shot Prompting enables the model to learn from the prompt without any additional parameters. In this way, it is continually learning.

They learn fast: When demonstrating in-context learning, Large Language Models - LLMs learn quickly because they do not require additional weight, resources, and parameters for training. It is fast in the sense that it doesn’t require too many examples.

Limitations and Challenges of Large Language Models - LLMs

Large Language Models - LLMs might give us the impression that they understand meaning and can respond to it accurately. However, they remain a technological tool and as such, Large Language Models - LLMs face a variety of challenges.

Hallucinations: A hallucination is when a LLM produces an output that is false, or that does not match the user's intent. For example, claiming that it is Human, that it has emotions, or that it is in love with the user. Because Large Language Models - LLMs predict the next syntactically correct word or phrase, they can't wholly interpret human meaning. The result can sometimes be what is referred to as a "hallucination."

Security: Large Language Models - LLMs present important Security Risks when not managed or surveilled properly. They can leak people's private information, participate in Phishing Scams, and produce Spam. Users with Malicious Intent can reprogram Artificial Intelligence - AI to their Ideologies or Biases, and contribute to the spread of Misinformation. The repercussions can be devastating on a global scale.

Bias: The data used to train Language Models will affect the outputs a given model produces. As such, if the Data represents a Single Demographic, or Lacks Diversity, the outputs produced by the Large Language Model - LLM will also Lack Diversity.

Consent: Large Language Models - LLMs are trained on trillions of datasets — some of which might not have been obtained consensually. When scraping data from the internet, Large Language Models - LLMs have been known to ignore Copyright Licenses, plagiarize written content, and repurpose proprietary content without getting permission from the original owners or artists. When it produces results, there is no way to track data lineage, and often no credit is given to the creators, which can expose users to copyright infringement issues.

They might also scrape personal data, like names of subjects or photographers from the descriptions of photos, which can compromise privacy.2 LLMs have already run into lawsuits, including a prominent one by Getty Images3, for violating intellectual property.

Scaling: It can be difficult and time- and resource-consuming to scale and maintain Large Language Models - LLMs.

Deployment: Deploying Large Language Models - LLMs requires Deep Learning - DL, a Transformer Model, Distributed Software and Hardware, and overall technical expertise.

Natural Language Processing - NLP

Computer Vision