What are Large Language Models (LLMs)

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.

Definition of Large Language Models (LLMs)

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.

Components of Large Language Models

A Large Language Model consists of multiple layers of neural networks that work together to process the prompts inputs and generate outputs.

These are:

• Recurrent layers: help the model to interpret information from the input text in a sequential manner. In static state, they are hidden, but when there is an input, they form a layer of each update.

This way the model can capture the inter-dependence between each word in a sentence.

• Feedforward Layers: are several fully connected layers that ‘apply nonlinear transformations to the input embeddings”. With the help of these layers, the model can interpret the input text better.
• Embedding Layers: they convert each input byte to a high-dimensional vector representation. The embeddings in this layer capture detailed information about the input words, ensuring a higher understanding of the prompt.
• Attention Layers: This is the layer that enables the model to focus on selective text. Due to this, the model can grasp the most relevant part of the input prompt. This essentially enables the most critical part of the question to get the most accurate answers.

The Best Large Language Models

In addition to the GPT-3 of ChatGPT mentioned above, some well-known LLMs are:

1. GPT-4: is the next generation to GPT-3 and GPT-3.5, and hence more accurate. It excels at summarization and text generation and also helps to create plugins, as also execute code and functions.
2. BERT (Bidirectional Encoder Representations from Transformers): This is Google’s LLM, trained on the massive data repository that Google functions on. It is very sensitive to inputs meanings and can provide very relevant and meaningful responses.
3. XLNet: was created by Carnegie Mellon University and Google. It is a state-of-the-art performer in the interaction between machine and human prompts.  It uses a “permutation language modeling” approach.
4. Llama 2: Recently released by Meta, it is the first Open source based LLM. Due to its open nature, it is freely available for research and commercial use.
5. Orca: From Microsoft, is a smaller model that is Open Source. Its learning technique differs from other models. It used progressive learning wherein it trains itself, using large foundation models like GPT-4. It learns by imitation, [progressively improving its own capabilities.
6. Bloom: is also an open source-based LLM and the first ever multilingual. It is also openly available for researchers, developers, and also enterprises to build useful applications on it, with no cost involved.

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.

7. T5 (Text-to-Text Transfer Transformer): With a catchy name like that, this is also a Google baby.

It can perform text-to-text transformations like language translations. It can do more language functions like summarizing, introductions, etc.

8. RoBERTa (Robustly Optimized BERT Pre-Training Approach): Meta has developed it. It is an improved version of BERT. It has added capabilities to perform more language tasks.

What are large language models used for?

LLMs can be used for several tasks requiring Natural language processing abilities. Here are some most common uses:

• Text generation
• Translation- Can be delivered by LLMs trained on multiple languages
• Content summary. Most LLMs can summarize huge blocks of information or blocks or multiple pages of text.
• Rewriting Classification and categorization of content.
• Sentiment analysis. Most LLMs can analyze content for better understanding of the user.
• As Chatbots: LLMs can use conversational AI to enable almost natural conversations. This is , in fact, the most common use of AI, and is very popular as ChatGPT, based on OpenAI‘s GPT-3 model.

Advantages and Challenges of LLM for Enterprise

• LLMs can create finely tuned personalized, and customized use cases.
• With proper training, one model can be used multiple times and create extremely specific personalized use cases.
• These are typically high-performing models that deliver low-latency responses.
• Their accuracy levels are very high given the massive data used for training them.

 There are, however, also some challenges that LLMs pose to enterprise usage.

• Firstly, there is a cost factor. Training on large data requires large amount of hardware to process all formats.
• Operating LLMs also costs the organization. After all this investment, there is always the risk of bias that creeps in through the ML training data.
• Enterprises may find the complexity of an LLM challenging. Due to this they can do very little when an AI hallucination occurs. This refers to an inaccurate response.
• In addition to these intrinsic challenges, emerging threats to LLMs have been seen recently. These are the Glitch tokens which are malicious prompts that lead an LLM to malfunction.

If enterprises can overcome these challenges, the value that LLMs can impart to accuracy, efficiency and cost optimization, is priceless.

Also Read: How ChatGPT is Transforming Enterprise Search with Natural Language Processing

Conclusion

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.

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