By Kanika Goswami - September 11, 2023 8 Mins Read
A large language model is the best choice to tackle huge big data analysis or such large-scale operative tasks.
A Large Language Model (LLM) is an advanced language model can use deep learning techniques on huge volumes of text data. It can generate text that mimics human writing ability.
These models can be tuned to human preferences. This is done using Reinforcement Learning with Human Feedback (RLHF). It can also perform various natural language processing tasks. In this article, we see what are Large Language Models, and what are the Best Large Language Models.
According to Gartner, the definition of LLM is: ‘A large language model (LLM) is a specialized type of artificial intelligence (AI) that has been trained on vast amounts of text to understand existing content and generate original content.’
These are essentially ‘foundational machine learning models’, that have the ability to understand natural language using deep learning algorithms.
Simply put, LLMs are first trained on a massive set of data. Thereafter, they can generate new content based on the training algorithms. The earliest LLM is believed to be the ELIZA language model, developed in 1966 at MIT.
This was about the same time as AI was taking shape. In a way, LLM is a part of AI. Such a large data set when used for learning increased the capabilities of AI models.
LLMs are trained on huge amounts of text data. That data could be from varying sources- books, articles, websites, or other written content forms.
Through this process, it learns to analyze the statistical relationships between words, phrases, and sentences.
This way, it can generate coherent and contextually relevant responses to prompts or queries. In other words, these models learn patterns and entity relationships in human languages. They can comprehend large amounts of textual data in context.
They can also identify relationships between entities and generate coherent and well written text.
These LLMs also are capable of performing many other language tasks. This could include language translations, sentiment analysis, and chatbot conversations, to name a few. The best-used example of an artificial intelligence (AI) tool trained by LLM is ChatGPT.
Developed by OpenAI, the GPT-3 has 175 billion parameters that it can answer on. Its model was trained on huge amounts of textual data on the net.
This has allowed it to understand languages, under various topics and fields. It can use this skill to produce text in many different styles in any style on demand. Its massive abilities result from special grammar that responds to certain prompts in text.
A Large Language Model consists of multiple layers of neural networks that work together to process the prompts inputs and generate outputs.
This way the model can capture the inter-dependence between each word in a sentence.
In addition to the GPT-3 of ChatGPT mentioned above, some well-known LLMs are:
This model has 176 billion parameters and can generate text in 46 natural and 13 programming languages.
Some interesting numbers- Bloom is trained on 1.6TB of text data, which, in volume, is text 320 times the complete works of Shakespeare.
It can perform text-to-text transformations like language translations. It can do more language functions like summarizing, introductions, etc.
LLMs can be used for several tasks requiring Natural language processing abilities. Here are some most common uses:
There are, however, also some challenges that LLMs pose to enterprise usage.
If enterprises can overcome these challenges, the value that LLMs can impart to accuracy, efficiency and cost optimization, is priceless.
There is no doubt that LLMs can add he value to enterprise operations.
Their ability to train AI applications to deliver much better solutions will be a critical value add to enterprise in the coming years.
LLMs have the ability for higher personalization of offerings and solutions. This will make these models a strong marketing ally.
However, one of the biggest fears they drive is their ability to disrupt job markets. They can perform all the repetitive tasks that humans do today. But once they do that, there will be a major impact on society.
On the one hand, their use will reduce costs and timelines and increase efficiencies of all operations. On the other, jobs will be lost; humans will need to re-skill and upskill if they want to stay employed.
For businesses, these advantages will come with newer adjustments and newer tools. These tools will use more AI to add efficiencies to their operations. These new solutions will need new resources with skills, so the employment market will balance out with reskilling.
So, enterprise and non-enterprise users need a balanced critical view of the technology. LLMs can bring about huge positive changes in human enterprise and society. They also have the power to cause massive disruptions. Adopting them with all awareness is the way forward.
Editor-in-Chief - Ondot Media With over two decades of experience as a journalist, Kanika is the mentor and guide for Ondot media’s editorial team. She has worked with global media brands like IDG (CIO magazine) and Indian media brans like Economic Times, and has specialized in Enterprise technology content for over a decade now.
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