Large language models (LLMs) have captured the attention of businesses, governments, and people worldwide.
As a result, companies are interested in developing their own large language model, and governments are grappling with how to regulate and control large language models.
Despite the growing interest in these generative AI models, there is still a lot of confusion about what these tools are and how large language models work.
This post will closely examine what large language models (LLMs) are, how they can benefit interested businesses, and the fundamental limitations and challenges associated with using a large language model.
What Is a Large Language Model (LLM)?
A large language model (LLM) is a deep learning algorithm capable of executing natural language processing tasks. Large language models are one type of generative AI, and they have arguably garnered the most public attention through the GPT large language model.
Large language models are sometimes referred to as neural networks. Neural networks are computing systems that are inspired by the human brain. These networks utilize nodes that are layered similarly to the neurons in a human brain.
It should be noted that large language models or neural networks are nowhere near as capable as a human brain. They are only modeled after what we understand of neural function, and to be fair, there is a lot that scientists still do not understand about how our human brains function.
A large language model must be pre-trained, rigorously tested, and fine-tuned to solve problems like text generation or question answering. The training data used for these Machine Learning models are massive, which is where the name large language model (LLM) comes from.
How Do LLMs Work?
Large language models (LLMs) rely on the abilities of a transformer model to generate text and complete specific tasks related to language. What is a transformer model?
The transformer architecture consists of two basic components: an encoder and a decoder. Text is input into the transformer and is turned into a tokenized input. This tokenized input is then decoded using mathematics to discover relationships between the tokens.
This transformation and subsequent decoding enables large language models (LLMs) to identify and recognize patterns that might otherwise elude the human eye. Transformer models work with self-attention mechanisms that enable them to learn more rapidly than traditional foundation models.
The self-attention mechanisms allow the large language model to account for context or consider different parts of the input sequence when generating predictions.
This is just a simple overview of how large language models (LLMs) work. There are additional complexities involved in these tools, but we will not explore the ins and outs of these complexities in this blog post.
Large Language Model Use Cases
While text generation is the most prominent and likely popular use case of large language models, LLMs can be used effectively in several other ways. For example, you could use a large language model to retrieve information.
Search engines like Google and Bing rely on LLMs to understand text input and produce relevant results. Newer AI tools like ChatGPT have been used widely to answer questions or find information.
Along similar lines, large language models are often used to power chatbots. Good LLMs mimic human language so effectively that they can help resolve most low and mid-level customer issues when implemented on a website.
Part of being an effective solution for chatbots is sentiment analysis. LLMs can also help organizations analyze the sentiment of textual data.
Large language models can also be used to generate code. After all, code is often similar to human language, albeit with different syntax rules.
LLMs are effective at handling a range of language tasks. Some tools are designed to handle specific tasks more effectively than others. You might get poor results if you’re trying to generate code with a large language model primarily built for text generation.
The Benefits of Large Language Models
Large language models can be used effectively to benefit businesses in several key ways. The top benefits of LLMs your business can experience include the following:
- Improve workforce efficiency
- 24/7 availability
- Constant improvement
Improve Workforce Efficiency
Large language models can drastically increase your workforce’s efficiency through automation and assistance in completing tasks. LLMs can automate rote, repetitive tasks like data entry and document creation.
By automating time-consuming, repetitive tasks, large language models give workers more time to focus on high-level tasks that require a human’s expertise and insight. In addition, LLMs can be used as a tool to complete tasks more efficiently.
For example, marketing employees can use LLM tools to help craft campaign messaging and draft communications. Software developers could use large language models to help them with coding tasks, using an LLM to write boilerplate code and check code snippets for errors.
When your organization’s workforce is operating more efficiently, your business can focus on innovation and the small details that separate your business from the competition.
24/7 Availability
A significant benefit of LLMs is that they are always available. This fact makes them perfect for tasks like customer service. Instead of paying employees to work overnight or closing customer service after a specific hour, LLMs can help you keep your customer support open all day.
Increased support availability can help deliver a positive customer experience. In addition, since LLMs have gotten so adept at sentiment analysis and text generation, they are often more than capable of handling low to mid-level customer issues without human intervention.
Constant Improvement
One of the best benefits of large language models is that they constantly improve as new parameters and data are added. Essentially, the more you use a large language model, the more effective it gets at completing its tasks.
If your business is considering investing in a large language model, this is a substantial benefit to consider. The more you use the tool, the better it will get. This is not to say that it will perform poorly out of the gate, but over time, performance should improve the more it is used.
How many other tools improve the more you use them?
The Challenges of Large Language Models
Large language models have gotten very adept at processing language to the point where some people erroneously believe these tools exhibit Artificial General Intelligence. These tools are incapable of genuine thought. It is important to remember that.
If you remember that these are just sophisticated natural language processing tools, you can avoid some of the challenges these tools still present. The top challenges to be aware of are:
- Hallucinations
- Bias
- Consent
Hallucinations
Just because a large language model outputs something does not make it accurate. LLMS can produce factually incorrect statements or entirely made-up information.
Remember, these tools are sophisticated language processors and predictors. They are trained to recognize patterns and return the most logical response based on the pattern.
There have been instances where a lawyer used an LLM, like ChatGPT, to write a legal brief. The issue is that the lawyer never fact-checked the brief, and ChatGPT invented several legal precedents that never happened.
If you are using a large language model to write or answer a question, make sure that you check the answer for accuracy. LLM hallucinations are more common than you might think.
Bias
If the training data used on the large language model exhibits bias, the model will likely learn and exhibit this same bias. There is a famous example that often gets cited during the example of model bias.
Amazon built a Machine Learning model to screen resumes and find the best IT and software development employees for its business. Amazon developers fed their model the resumes of all the top IT and development professionals they could find to train it on what to look for in a resume.
The problem? The overwhelming majority of these resumes represented men to the point that the model learned that women are less preferable candidates for these professional roles, which is obviously false.
Of course, Amazon didn’t intentionally bias their model, and they immediately shut it down when it became clear what happened. However, this example illustrates how easy it is for bias to enter into a model, even unintentionally.
Consent
Large language models are trained on vast amounts of information. The issue is that not all of this training data is gathered consensually. This can lead to large language models plagiarizing or repurposing protected content.
This issue has become a more significant concern lately as models like ChatGPT become more mainstream. Several artists and creators have started legal action against the developers of these tools for using their work without permission.
Final Thoughts
Large language models have successfully captured our collective imagination. From Google’s Bidirectional Encoder Representations from Transformers (BERT) to OpenAI’s ChatGPT, LLMs have made impressive progress and are better than ever.
It is challenging to develop and maintain large language models, so many companies prefer to use an existing solution like BERT or ChatGPT. In addition, a large language model’s performance can vary significantly depending on the task and the input.
This can make it difficult for businesses to navigate the popular large language models to find the one that best suits their needs. Contact an experienced Machine Learning and AI development partner like Koombea to learn more about large language models.
