In today's daily life, we often use AI chatbot technology such as ChatGPT, Gemini, Claude, or others. However, do you know the technology behind it? One of these technologies is the large language model or abbreviated as LLM.
In this article, we will discuss this technology in basic terms, starting from the definition, how it works, to its benefits for life. Read this article until the end, okay.
What is LLM?
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| What is LLM? |
Large language models (LLMs) are deep learning models designed to understand, translate, and generate human-like language. LLMs are trained on large amounts of public domain data with millions or billions of parameters, so that the text they generate sounds like human writing.
LLMs are used in the broader domain of natural language processing (NLP), which is a branch of artificial intelligence (AI) that deals with the interaction between computers and human language. NLP is used to analyze, understand, and generate human language, allowing machines to read and interpret text, speech, and other forms of communication.
LLMs serve as the underlying power behind some of the most widely used generative AI (GenAI) tools today, such as ChatGPT, Google Bard, and Jasper. Much of the recent growth and commercial investment in GenAI can be attributed to technological advances in large language models, such as the availability of transformer model architectures, new algorithmic innovations such as attention mechanisms and optimization techniques, and the accessibility of open-source frameworks such as TensorFlow and PyTorch.
A large language model literally means a large language model. However, to make it easier to understand, let's imagine this LLM as someone who diligently comes to the library and reads books. The library that is visited is filled with various books from various sciences. These books certainly contain a very large amount of text when combined. Well, LLM learns from millions or even billions of words. He is like a super genius whose brain is able to learn anything!
From these lessons, LLM is finally able to read the patterns depicted in the text. It's like you who know that fairy tales often open with "Once upon a time ..." or continuing the lyrics of the children's song "red, yellow, green, in the sky that ..." with the word "blue".
That's LLM, it's a large language model. As the name implies, this model is trained using millions to billions of existing words to be able to predict sentence patterns to create new texts. Because it is trained using a lot of data, this model can also perform various tasks and produce text with language like humans.
How Does LLM Work?
As explained above, LLM can process millions or even billions of words. How does it work? This model works by making predictions about the appearance of the next word. When you write "once upon a time ..." as LLM input, it will predict the next word to complete the sentence. The predictions made are based on millions or billions of sentences from training data.
The more often the word is used to continue the input sentence in the training data, the higher the chance that the word is considered the right choice by the model. For example, "once upon a bright day" may be more common than "once upon a furious day".
The model uses parameters (weights and biases) to calculate the relationship between words. This measures how well the word matches other words in a sentence. There are millions or even billions of parameters in a model. This is so that the model can better understand the context and ultimately be more natural in creating text.
Today's LLM is supported by an architecture called Transformers. Before Transformers, words were processed one by one in sequence so that the model was less able to understand the context of long sentences as a whole.
After Transformers arrived, each word had a self-attention mechanism, which is a weight measurement process by taking into account the relationship of each word to all the words in the sentence.
This makes this technology not only able to understand the context briefly, but also improve the model at a higher level. Transformers make LLM able to understand longer contexts - for example in paragraphs, even documents - without losing the full meaning.
Why are large language models so good?
Creating a high-quality LLM starts with the dataset that is presented to it and used to train the LLM. The more diverse and comprehensive the dataset, the better the LLM will be at generating contextually relevant and human-like text.
A diverse and comprehensive training dataset typically extracts data from a variety of sources on the internet, such as articles, websites, books, or other textual resources provided by the person or business developing the model.
One problem with sourcing training data from the internet is that there is a risk that the LLM will generate misleading or biased text. Because the LLM learns from the training data it is exposed to, if there is biased information, there is a chance that the text generated by the LLM will inherit that bias.
Reinforcement Learning from Human Feedback (RLHF) is a process that can help improve the quality of LLM responses. In RLFH, after the model generates a response, a human reviews the response and rates its quality. If the response is of poor quality, the human generates a better response.
All of the human-generated responses are then fed back into the training dataset to retrain the model to learn high-quality responses.
Additionally, the emergence and adoption of retrieval-augmented generation (RAG) is helping LLM deliver more accurate and relevant AI responses. In the RAG methodology, a large underlying language model is connected to a knowledge base—usually proprietary, company-specific data—to provide up-to-date information with relevant context.
What are the benefits of large language models?
At this time, LLM is very helpful in everyday life. If you have used ChatGPT or Gemini to help with your daily tasks, it means that you have used this technology in your daily life. Here are the benefits of using it in life.
Large Language Models (LLMs) have brought about a major shift in the world of artificial intelligence, especially in their increasingly impressive text generation capabilities. Here are the key advantages that make LLMs a milestone in the evolution of AI:
1. Increase Productivity
The use of LLM can help us to increase productivity. Imagine many time-consuming text-related jobs can be completed in just minutes, even seconds.
For example, if research used to take days or even weeks, with this technology the work only takes a matter of hours. Another example is when replying to messages or questions from customers can take hours to complete, with the presence of LLM it can be completed in minutes or even seconds.
2. Sharpen Creativity
Interaction with LLM is generally done by entering certain prompts or commands. Some say that making prompts is also an art. This is true because creating prompts that can produce output according to your wishes is not easy.
With this, we can hone our language skills and create various new texts with the help of LLM. In creating new written works, the results created by this technology must be re-checked by humans. This process will hone our creativity.
3. Expanding Accessibility
LLM has bridged various groups with its sophistication. Starting from language differences to differences in access by people with disabilities, all can be connected with the existence of LLM.
Many platforms expand access with LLM technology, starting from Google Translate to translate languages, live caption technology on streaming platforms or video conferences to facilitate deaf access, to Be My Eyes which helps the visually impaired.
4. Better Text Generation Capabilities
LLMs not only generate natural and readable text, but they also have a good understanding of the context of the input. They can generate appropriate and informative responses, creating a more natural interaction between humans and AI systems. This opens the door to wider use in a variety of applications, from virtual assistants to automated writing.
5. Language Diversity and Extensive Skills
One of the key strengths of LLMs is their access to a vast amount of text data from a variety of sources, such as news articles, books, and scientific journals. This gives them extensive language skills and in-depth knowledge in a variety of domains. This capability enables them to provide accurate and relevant information, incorporating the knowledge they gain from their training data.
6. Adaptable and Personalization
LLMs can be customized to suit a variety of needs and contexts, from industry to customer service. They can be given additional hints or guidance to generate text that suits the user’s preferences and needs. This adaptability and personalization make LLMs a highly flexible and effective solution to meet the varying demands of users.
What are the applications of large language models?
Here are some examples of applications of LLMs that have changed the way we interact with technology and information:
1. Fast and Effective Information Search
LLMs are used in search engines like Google to provide relevant and comprehensive information in response to user queries. They can retrieve information from multiple sources, summarize it, and present it in a conversational style that is easy to understand. This has changed the way we obtain and consume information online.
2. Sentiment Analysis
In Natural Language Processing (NLP) applications, LLMs help businesses analyze the sentiment of text data. This helps understand customer perceptions, market trends, and feedback in a holistic manner. Thus, LLMs help in better decision making and more effective strategies.
3. Text generation
LLMs like ChatGPT are used to generate text based on the input given. They can write poems, stories, or other creative content in a style that suits the user’s preferences. This opens the door for creativity and innovation in content writing.
4. Code generation
In code generation, LLMs understand natural language patterns and enable the generation of programming code from natural language prompts. This speeds up the software development process and increases programmer productivity.
5. Smarter Chatbot Interactions
LLMs enable customer service chatbots to interact with customers more intelligently. They can understand the meaning of a customer’s question or response and provide a response that is contextual and appropriate to the need.
6. Role of LLMs in Healthcare
LLMs have the ability to understand the structure of proteins, molecules, DNA, and RNA. This helps in vaccine development, drug discovery, and disease prevention. LLMs contribute greatly to the advancement of healthcare and medicine.
How Large Language Models Are Used
Large language models are used in a variety of ways by businesses, professionals, and everyday users. Popular LLMs like OpenAI’s Generative Pre-trained Transformer (GPT) have been trained on very large and diverse datasets from the internet, meaning they are often used to complete a variety of tasks without task-specific training, such as
- Answering questions
- Summarizing documents or text
- Interpreting tables and charts
- Generating creative content, such as stories or poems
- Translating languages
Businesses can also refine and apply LLMs to perform specialized applications and tasks across industries such as:
1. Automotive
LLMs are a critical component in creating next-generation vehicles that use GenAI assistants for drivers and passengers.
2. Customer service
LLMs are used to automate aspects of customer service. For example, businesses can deploy chatbots that can understand and respond to customer questions in human-like language. This can reduce response times, improve efficiency, and increase customer satisfaction.
3. Education
GenAI powered by LLMs in education is used to personalize content, provide near real-time feedback, and guide training and skills development.
4. Energy
GenAI powered by LLM is used in the energy sector to enable more empathetic customer experiences when using chatbots and provide enterprise-specific personal assistants; simulate and generate optimal grid configurations, test various demand scenarios and outage response strategies, and plan for the integration of new energy sources. It can also be used to ingest and analyze data from a wider range of sources for advanced analytics use cases in support of predictive maintenance.
5. Financial services and banking
LLM is widely used in banking and financial services to process large amounts of transactional data to help detect and prevent fraud and mitigate risk. It is also used to analyze financial news articles and social media posts to identify sentiment and make predictions about stock prices, and deploy AI chatbots and financial assistants for customers.
6. Government
GenAI powered by LLM is used in government agencies to create personalized AI chatbot experiences with the ability to better understand user needs and provide more contextual information, as well as enable automation and informed decision-making in the office, lab, and field.
7. Healthcare
In healthcare, LLMs are used to process and analyze medical text, such as electronic health records, to extract critical information and improve patient care. LLMs can also generate reports or offer medical treatment recommendations.
8. Manufacturing
GenAI-enabled chatbots and self-service portals help improve customer support while reducing live calls to maximize employee time. LLMs are also used to improve the customer experience by personalizing communications, marketing campaigns, and emails for greater engagement.
9. Media and entertainment
LLMs are used to analyze large amounts of content and data to make personalized recommendations, improve content creation, and better understand audience behavior.
What are some of the challenges of using large language models?
While the use of LLMs brings significant benefits to businesses and users, LLMs also come with challenges and risks that cannot be ignored:
1. Bias
LLMs are trained and learn from existing data that may have biases. Therefore, there is a potential for LLMs to inherit these biases and propagate them in subsequent generated text.
2. Environmental impact of training
Training large numbers of LLMs requires substantial computing resources that can potentially have long-lasting, detrimental environmental impacts. For example, studies have shown that training a single common LLM, such as Google’s Bidirectional Encoder Representations from Transformers (BERT), on a GPU can emit as much CO2 as five cars over their lifetime.1 Efforts are underway to mitigate these impacts and make AI more sustainable and use AI to improve overall business sustainability efforts.
3. Interpretability
It is currently difficult to understand the decision-making process of LLMs and interpret how it occurs as well as the output it produces. This is due to many factors, including the complex nature and scale of LLMs, the size and diversity of the datasets used to train them, and the current lack of mature explainability tools. However, efforts are underway within the AI community to increase the transparency and explainability of AI models.
4. Responsible use of AI
Additional challenges in using AI include ethical and societal implications. Leaders in AI innovation are collaborating and committing to responsible AI practices that are transparent, inclusive, and accountable to help raise awareness of AI’s potential impacts on society and ensure that advances in AI continue to advance society.
What is the future scope of large language model?
Just as the future of AI technology is rapidly evolving and changing, so is the future of LLMs. Researchers are continually exploring new ways to improve LLMs based on current limitations and challenges. Here are some areas of focus:
- Increasing efficiency: As LLMs grow in size, complexity, and capabilities, so will their energy consumption. Researchers are developing ways to make them more efficient, reducing their computational requirements and environmental impact.
- Mitigating bias: Researchers are taking a variety of approaches to mitigating bias because this is a complex and ongoing challenge. These approaches include but are not limited to curating and diversifying datasets, forming industry and academic partnerships to share best practices and tools, conducting user studies and gathering feedback from diverse user groups to identify bias and iteratively refine models, and implementing techniques that detect and filter biased content.
- Exploring new types of architectures: Large companies are actively researching new LLM architectures, pre-training these models, and working to make them available for anyone to use and improve.
Conclusion
Large language models (LLMs) are artificial intelligence models used to understand and generate human language. LLMs are used in a wide range of applications, such as text generation, question answering, content translation, and creative content creation. Businesses use LLM-based applications to help improve employee productivity and efficiency, provide personalized recommendations to customers, and accelerate ideation, innovation, and product development.
With their wide range of applications and significant impact, Large Language Models have become a key driver of transformation in business, technology, and science. Their ability to understand and process human language opens the door to new innovations and further advancements in many areas of our lives.
