A quick introduction to the Large language model (ChatGPT)
Introduction
Over the past decade, major breakthroughs have been made in the field of Artificial Intelligence (AI). When it comes to AI, there are a number of subfields, like Natural Language Processing (NLP). One of the models used for NLP is the Large Language Model (LLMs). LLMs are designed to process vast amounts of text data and use advanced neural network architectures to learn the patterns and relationships between words, phrases, and concepts in natural language. This means that they have the ability to understand the context and meaning behind words and phrases. As a result, LLMs have become a key tool for a wide range of NLP applications.
ChatGPT, a chatbot developed by the OpenAI team, is an example of an LLM. It has been gaining a lot of attention lately due to its ability to create human-like text. You can even try this out on the OpenAI website to get a feel.
If you are interested in learning more about how NLP works, you can learn how to write Python code. Experts recommend Python as one of the best languages for NLP as well as for machine learning and neural network connections. R programming languages are also popular amongst researchers and developers working on Large Language Models. Both of these languages have extensive libraries that will get you started with the basics of machine learning. Next, we will look at how exactly LLMs work.
How do LLMs work?
LLMs work by taking in a huge amount of text data and then processing it and learning patterns between the words and patterns. After understanding the meaning of sentences, they can then generate their own sentences, based on the training data that were given to them. This data comes everywhere, including articles, blogs, news sites, and journals. Due to the vast amount of data it consumes, it can derive patterns in the given text to generate human-like text. It would be impossible for humans to do the same because we have limited memory and processing ability. On the other hand, computers can store and process huge quantities of data.
Diagram for Large Language Model
The model itself works by having a neural network that is made up of connected nodes, allowing it to model relationships between words and phrases in natural language. The training data act as the input for this model, and the quality of the output will depend on the data it was trained on. In the case of ChatGPT-3, it analyzes conversation data from the internet, like conversations from Reddit forums. On top of that, human trainers are also employed to fine-tune the data given to the model by providing feedback on the quality and its relevance. The way an LLM works is similar to how a child learns a language; when a child is placed in an environment where everyone is speaking that language, he will learn and mimic the speaking behavior of the people around him. If that child is also guided by a teacher that gives him feedback on the sentences that he produced, he will learn to accurately produce the sentences in that language.
What are LLMs used for?
LLMs are used in a variety of ways, and some of them are:
- Language translation: LLMs can be used to translate words from one language to another quickly. It does this by comparing the two languages and trying to translate it on a sentence-by-sentence basis through what is called Parallel Corpora. LLM does translation in two ways. First by direct translation, and second by encoder decoder translation. Both of these techniques use a deep learning approach for translation.
- Content creation: The output generated by LLMs can be used as text content for your product. Examples of this include articles, product descriptions, brochures, and other types of written content. ChatGPT is an excellent tool for this. It can produce high-quality text content that is indistinguishable from content made by humans. Consider using this if the work you do consists of writing content for your users.
- Chatbots: One major application of LLMs is in using it for Chatbots. Many companies are already using ChatGPT as part of the customer support chatbot tool to serve their customers in the best way possible by giving them accurate responses. Tech leaders are also considering ways to develop their own language model to suit their business needs by giving it relevant internal data.
- Summarization: Some LLM can be used to summarize long articles by generating a shorter version of it, without compromising the intended message. ChatGPT does this by gathering posts submitted to Reddit, with human-written summaries. Then, trainers fine-tuned the summaries so that the model generates high-quality summaries through the process of reinforcement learning.
Fields that are using LLM
Based on the applications mentioned previously, LLMs are currently being used in these sectors:
- Technology businesses: a big part of a tech business is dealing with customers. Managers and leaders in the tech sector are already looking for ways to streamline the process of communicating with customers through ChatGPT. In addition to that, LLM can also be used to write content for businesses. It can be used to write product descriptions, mission statements, and other written text. Another interesting way it can be used in the tech sector is for writing code. Programmers looking for an efficient way to write and maintain code can use ChatGPT to analyze existing code bases or ask it to write common scripts. This is now possible with the advancements that have happened over the past few years.
- Healthcare: LLMs can be used in the healthcare sector in a number of fascinating ways. One of the use cases is to predict virus variants, by being trained on large amounts of genomic data and then using it to generate new sequences. Other ways include using LLM to diagnose health issues, and then identify potential treatments. It does this by looking at huge quantities of medical data. As a result, this makes medical diagnosis more accurate and ultimately saves lives. LLMs have the potential to revolutionize the healthcare industry.
- Retail: The retail sector can also benefit from using LLM. One way it can be used is to help businesses better understand customer behavior and preferences. By analyzing customer data such as search queries and online interactions, LLMs can provide insights into what products and services customers are looking for and how they prefer to interact with the business. This information can be used to optimize marketing campaigns, personalize the customer experience, and make more informed business decisions.
What are the challenges in LLM?
Machine learning models, including LLM, are only as good as the training data given to them. This means that if you train it with low-quality data, it will produce low-quality output. This can be problematic when the stakes are high, and there can be no tolerance for error. Although what constitutes low or high-quality data can be subjective, some characteristics of high-quality data are accuracy, relevance, and diversity. Characteristics of low-quality data include incompleteness, bias, and inaccuracy.
To illustrate, consider training a model to create grammatically correct sentences. A low-quality data would look like this:
Dis tex iz nut good bcoz it contayns spell1ngs erorIn contrast, high-quality data looks like this:
This text is good because it doesn’t have any errorsHuman trainers are needed to supervise and adjust the data to ensure that it has high quality. Another drawback is that scaling and maintaining large amounts of data can be difficult and expensive. Currently, most LLM work is done by researchers and facilitated by big companies that have the resources to do so.
Recently, ChatGPT has been criticized for producing biased content, as the training data have been said to contain inherent biases. Another valid concern is how bad actors are going to use it for malicious purposes, such as using it to generate content for spreading misinformation or propaganda to influence public opinion.
What does this mean for you?
We have now reached the end of the article. Now that you have an understanding of how large languages work, you might want to know what the implications are for you. Significant progress has been made in LLM in recent years, and experts believe that it will change the way we communicate in the future.
Since there are going to be a lot of AI opportunities in the future, you might want to consider learning how it works and how to deploy and create a model. The most popular language for machine learning models is Python, as it has libraries such as Keras and Tensorflow that can be used to create neural models. There are already many applications of AI, including image processing, and there are going to be more in the future.
What are your thoughts on Large Language Models? Please share your thoughts below. You can also learn more about Education Ecosystem here.
References:
[1] Angie Lee, What are large language models used for?, Nvidia blog
[2] Geetika Gupta, Speaking the Language of the Genome: Gordon Bell Winner Applies Large Language Models to Predict New COVID Variants, Nvidia blog
[3] Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, Paul Christiano, Learning to Summarize with Human Feedback, OpenAI blog
[4] Josh A. Goldstein (1 and 3), Girish Sastry (2), Micah Musser (1), Renee DiResta (3), Matthew Gentzel (2), Katerina Sedova (1) ((1) Georgetown’s Center for Security and Emerging Technology, (2) OpenAI, (3) Stanford Internet Observatory), Forecasting Potential Misuses of Language Models for Disinformation Campaigns — and How to Reduce Risk, OpenAI blog
[5] Alex Tamkin and Deep Ganguli, How Large Language Models Will Transform Science, Society, and AI, Stanford University
