Artificial intelligence (AI) is transforming many aspects of our lives. One of the most exciting developments in AI is the rise of large language models (LLMs). LLMs are machine learning models that can process and understand natural language, such as the text in books, articles, and websites.
You’ve undoubtedly heard about ChatGPT and similar tools which are based on LLMs. There are a lot of nuances to each implementation of AI tools using LLMs which add an interesting dynamic. This is also why there is a lot of confusion about what an LLM is and how they are used.
Let’s take a quick look into how LLMs work and where there are very interesting ways to use them safely.
What are Large Language Models?
LLMs are machine learning models that are trained on massive amounts of text data. They use deep learning algorithms, including neural networks, to analyze and understand natural language. By processing large volumes of text, LLMs can learn the rules and patterns of language. This includes not just words themselves but grammar, syntax, and semantics like voice/style of the writer if given enough data to learn from..
How do Large Language Models work?
LLMs work by processing text data and generating a numerical representation of the language. This numerical representation is known as an embedding. It captures the meaning and context of the text and then uses these embeddings to perform tasks, including language generation, language classification, and language translation.
Training an LLM is very interesting as we’ve seen with platforms like the recently released OpenAI GPT-4. Researchers typically train an LLM with massive sources of text-based data, like Wikipedia, news sites, highly ranked blogs, and academic sites and books. This data is fed into the model to train it to predict the next word in a sentence based on the previous words. This is how the LLM learns to understand the context and meaning of the text.
Applications of Large Language Models
LLMs have a wide range of applications in both industry and academia. One of the most common applications is natural language processing, where LLMs are used to extract meaning and insights from text data. For example, LLMs can be used to automatically classify documents, summarize articles, and analyze sentiment.
LLMs also have applications in machine-based translation. They can translate text from one language to another with a surprising level of success. LLMs are already being used in chatbots, digital assistants, and other conversational AI tools to understand and respond to user queries.
Short form content like ad copy, emails, web page content, and prompts for inspiring writers for longer content creation have proven to be the first wins with LLMs. It’s easier for an AI language tool based on LLM to create these more simple content styles. It can get challenging as content grows in length, complexity, or on what type of content you are creating.
Limitations and Challenges of Large Language Models
The most obvious challenge with LLM development is the training data. A language model is only able to predict language patterns based on what it learns from training data. Training the LLM also includes things like voice, style, and even opinion, and potentially bias. This has been a contentious issue as LLMs have become more widely adopted and put into use.
Coded bias and training data that does not have enough of a diversity in representative sources can create a challenge where language models will work very well, but are actually creating new language content which is incorrect or limited in understanding.
The Challenge of Accuracy and Truth
Now think about the challenge with LLM use is that content generation does not include fact-checking, or validation of technical accuracy. There are very authoritative sounding sentences generated which may actually contain inaccuracies or even blatant false information. It may not be a significant risk as a percentage of the overall content being created, but it’s a non-zero amount of risk and requires mitigation like human editors and validation.
There is also the issue of content length and the model not being aware of where it is relative to the flow of the content. Your LLM does not understand readability, callbacks, and many nuances that come into play with anything of a moderate length. If you are using tools that create content based on LLM you will likely have already found it repeats itself or loses context fairly easily.
These are the obvious challenges, but one is particularly interesting that requires more of a deep dive. This is around the potential risk with intellectual property and copyright When content is created based on source information without citing the origin, there are potential issues that will likely be a whole new area of IP law that are about to become very active. We will have much more to dive into on this specific topic in an upcoming blog.
It’s even a risk for content discoverability which has become know based on discussions shared by the team at Google about how LLM detection can influence search ranking.
Large language models are a powerful and exciting development in the field of AI and machine learning. By processing and understanding natural language, LLMs have an incredible potential to transform a wide range of industries and applications. AI continues to advance at an exponential rate in recent years and we can expect to see more and more innovative uses of LLMs in the years to come.
There will be advantages to using these types of models in some specific use-cases. At the same time, there are some inherent limitations and risks that you should be accounting for. LLM and AI text generators are already widely used, and those risks are being realized the hard way by some folks.
It’s an exciting opportunity to make the best use of these innovations. Since we are in the business of creating highly-engaging, original technical content, you can easily guess that we are deeply involved in research and development of the risks and opportunities created by these tools ourselves.