Large Language Models (LLMs) are a type of artificial intelligence (AI) systems that have gained widespread attention in recent years, and skyrocketed in popularity with the release of Open AI’s release of Chat GPT in late 2022. These models are designed to process and understand human language, which allows them to perform a variety of tasks, from writing coherent and grammatically correct sentences to answering complex questions.
At their core, LLMs are essentially giant supercomputers that process language. The supercomputers took in vast amounts of text data, analyzed it, and are now using that analysis to generate new text that is coherent and contextually relevant. This generation ability is why they are called Generative AI models.
In the early days of AI research, scientists focused primarily on building systems that could solve specific problems, such as playing chess or recognizing faces in photos (remembers the infamous series of chess matches between IBM’s Deep Blue and Gary Kasparov? ). Now that processing powers of computers are much better, researchers have turned their attention to developing more general-purpose systems that can understand and interact with the world in a more human-like way. This is where LLMs come in.
So the internet is filled with vast amounts of data about almost anything. By analyzing this data, LLMs can gain a deep understanding of the nuances of language and the ways in which people use it to communicate.
History of LLMs
To understand the development of LLMs, it’s important to first understand the history of natural language processing (NLP), the field of AI that focuses on teaching computers to understand and interpret human language. NLP has its roots in the 1950s, when researchers first began exploring the possibility of creating machines that could translate one language into another.
Early efforts at machine translation were largely based on rules-based systems, in which programmers manually created sets of rules to translate text from one language to another. These systems were largely unsuccessful, however, due to the complexity of human language and the difficulty of capturing all of its nuances in a set of rules.
In the 1990s, a new approach to NLP emerged, based on statistical models and machine learning algorithms. This approach focused on training computers to recognize patterns in large datasets of text, allowing them to identify the most likely meaning of a given word or phrase based on its context.
The breakthrough came when Microsoft and Google began exploring the use of neural networks for natural language processing in early 2000s. Neural networks are a type of machine learning algorithm that are inspired by the structure of the human brain. By using multiple layers of interconnected nodes, neural networks can learn to recognize complex patterns in data, making them well-suited for tasks like natural language processing.
In 2010, a team of researchers at the University of Toronto created a breakthrough in the field of NLP with the introduction of the first “deep learning” language model, known as a deep neural network. This model, which was trained on a massive dataset of text, was able to generate coherent and grammatically correct sentences, paving the way for the development of even larger and more sophisticated language models.
How LLMs Work?
The very basic answer is LLMs work by processing and analyzing extremely large amounts of data and then using that information to generate new, creative and meaningful text.
The more complex involves a technique called word-embedding to convert each word into a vector (a mathematical representation of the word’s meaning.) This vector contains information about the word’s context, as well as its semantic and syntactic properties.
The model then uses these vectors to build a representation of the meaning of a sentence or paragraph, by combining the individual vectors of the words in the sentence or paragraph. This representation is known as a “hidden state,” and it is used as the basis for generating new text that is similar in meaning and tone to the input text.
This is where it gets interesting (in case it wasn’t interesting enough so far). The process of generating new text is known as language generation, and it typically involves the use of an algorithm known as beam search. Beam search is a method for finding the most likely sequence of words that would follow a given input sequence, based on the model’s hidden state. Yes, so it all boils down to finding the next word in a sequence of meaningful word chain.
so, very basically, LLMS are giant computers that try to fınd out the next word after every word.
If an LLM is trained on a dataset of news articles, it can use that knowledge to generate new articles that are similar in tone and style. However, if the model is then trained on a dataset of scientific papers, it can adapt to this new type of input and generate text that is more technical and specialized. That is why you’re seeing new apps popping up daily for various scenarios like “chat assistant for your company’s internal docs” or “chat assistant for your medical records”.
Challenges of LLMs
Despite their impressive capabilities, LLMs are not perfect, and there are several challenges that need to be addressed in order to improve their performance. One of the main challenges is the issue of bias, which can arise when the model is trained on a dataset that contains a skewed representation of a particular group or topic. A good example of this would be Chat GPT’s recent labeling of Elon Musk as a more controversial figure than Che Guevara.
Another challenge is the issue of catastrophic forgetting, which occurs when the model is trained on a new dataset and begins to forget what it has learned from previous datasets. This can lead to a degradation in performance over time, as the model becomes less effective at generating coherent and contextually relevant text.
Applications and Use Cases of LLMs
One of the key advantages of LLMs is their versatility, which allows them to be used in a wide range of industries and applications. Here are 5 uses cases of how LLMs are being useful today:
1. Healthcare
LLMs are being used to analyze medical records, identify patterns in patient data, and even make diagnoses. For example, researchers at MIT used an LLM model to extract useful and important data from electronic health records to help with prescriptions in late 2022.
2. Finance
As with any new technology, folks in finance circles are the pioneers in trying to find implementations of it to increase their profits. SambaNova Systems recently announced “GPT Banking model“, that can leverage LLMs power to make sentiment analysis, entity recognition and language generation for banks to streamline their customer support workflows.
3. Entertainment and Media
LLMs are being used to create new forms of content, such as music and video. For example, the music streaming service Amper Music uses an LLM to generate original music tracks based on the user’s preferences and mood. Shutterstock was fast to move in and bought Amper Music and Amper’s music content generation services can now be used via Shutterstock.
4. Customer Service
It is not a surprise that LLMs found themselves a place in customer support, where automation is pretty important (automatically answering common customer questions and resolving issues more efficiently). For example, the chatbot company Ada is using LLMs to power its customer service chatbots, which can answer a wide range of questions and provide personalized recommendations.
5. Language Translation
LLLMs are already used for improving machine translations that are much more contextually relevant than we used to 2-3 years ago. Google Translate already uses an LLM to generate translations that are more accurate and natural-sounding than previous machine translation systems (yes, obviously we will make a post about Google Bard soon 🙂 ).
Ethical Considerations
As with any new technology, there are several ethical considerations that need to be taken into account when it comes to LLMs. Here are a few of the key issues:
1. Bias
One of the main concerns with LLMs is the potential for bias. Because these models are trained on large datasets of text data, they can sometimes perpetuate existing biases and stereotypes. For example, an LLM that is trained on a dataset of news articles that contain a disproportionate number of articles about men may be more likely to generate text that is biased against women. See the famous story about the 3 day life-span of Meta’s highly biased LLM bot here.
2. Privacy
LLMs have the potential to generate highly personalized and contextually relevant text, which raises concerns about privacy. For example, an LLM that is used for customer service may be able to generate text that reveals sensitive information about a customer’s personal life.
3. Misinformation
Since LLMs have the potential to generate highly convincing and contextually relevant text, they could be used to create fake news or spread misinformation. For example, an LLM could be used to generate a fake news article that appears to be legitimate. An upside to this is though, LLMs can also be used to detect fake news, so yes, it is AI fighting AI like in the movies!
4. Copyright and Ownership
LLMs are generative tools, so they can generate or create new ideas. But they do it based on great amount of data. For example, if an LLM is used to generate a song or a piece of writing, who owns the rights to that content? And what would stop an artist claim that LLM copied his style to produce that piece? We think that, both for LLMs and AI Art Generators, copyright will be one of the most important issues that will be discussed in the near future.
