Understanding Large Language Models: The Brains Behind Modern AI

Artificial intelligence (AI) has grown from a specialized tool into an essential part of how businesses operate. Companies now use AI to make their processes more efficient, improve decision-making, and find new ways to grow. From managing supply chains to analyzing customer behavior, AI is helping businesses solve problems and adapt to change in practical ways. 

According to a McKinsey survey, 65 percent of respondents reported that their organization regularly uses Generative AI (Source: McKinsey). Generative AI's ability to understand and process human language is one of the key factors driving its adoption worldwide.

At the core of this process are large language models, which are reshaping how we communicate with machines. It is not surprising that the global large language model market is set to reach USD 36.1 billion by 2030, expanding at a CAGR of over 33% (Source: MarketsandMarkets).

This article provides an in-depth exploration of large language models, covering how they function, their advantages and disadvantages, and the innovations expected in the future.

What is a Large Language Model?

Large language model is a machine learning model capable of performing tasks such as textual analysis, text generation, and simulated conversational language. Trained on a vast corpus of text, it learns to recognize general language patterns, including vocabulary, style, context, and even the implied meaning behind messages.

For instance, if you type “Once upon a time, there was a princess,” ChatGPT may respond with “Once upon a time, in a village, there lived a girl.” This understanding of context and subtle nuance makes massive language models highly intelligent.

Why is Large Language Model Important?

The popularity of terms such as artificial intelligence, LLM, and AGI has increased significantly since 2022, owing to the culmination of decades of research in AI.

These models are highly flexible: a single model can perform multiple tasks, from answering questions to summarizing large documents. They play a crucial role in Generative AI (Gen AI), where they generate content based on user input.

From healthcare to legal, large language models are helping us save time and effort on repetitive, redundant, and complex tasks. For instance, the adoption of generative AI is rising steadily in the healthcare sector.

AI has already been used in healthcare to predict adverse events and optimize operating room schedules. It can simplify complex tasks, reduce errors, and quickly provide doctors with years of clinical data while upgrading outdated healthcare systems.

How do Large Language Models Work

Large language models employ machine learning approaches based on neural networks to process and output language like human language. 

Central to this advanced model are transformers, a type of neural network architecture that has revolutionized natural language processing by allowing models to simultaneously analyze the relationships between all words in a sequence.

One of the biggest challenges in developing AI models is the need for extensive data labeling. Also known as data annotation, this tedious process involves adding labels to raw data and enabling machine learning models to understand it.

Large language models are trained through unsupervised learning; therefore, they understand patterns, eliminating the need for extensive data annotation. Furthermore, considering the comprehensive training they undergo, large learning models can be used for multiple use cases. Models that undergo extensive training are known as foundation models.

Foundation models can work autonomously or semi-autonomously without rigorous instructions and continuous human involvement. However, enterprises often have specific use cases that require the model to be used in a particular way or for a single use case. These foundation models are then ‘customized’ on those specific use cases via several methods, including prompt tuning and adapters.

Large Language Model Parameters

Large language models are built upon the foundation of several parameters. These large language parameters essentially determine the behavior of the LLM agent–how it understands language, what output it produces, what idioms it understands, how long a sentence should be, choice of words, etc.

Parameters allow LLMs to understand and converse with users dynamically. Enterprises use these parameters to fine-tune LLM for specific use cases or applications.

Let us look at the common parameters in LLM to understand this in detail.

Temperature

Certain industries, such as legal and medical, require formal, accurate, and to-the-point text. On the other hand, the marketing sector requires a mix of formal, creative, or even humorous text. End-users can fine-tune LLMs by adjusting their temperature parameter.

They can choose between high and low temperatures. High temperatures produce output that reduces redundancy and predictability, while low temperatures make the model consistent and predictable. By adjusting the temperature, end-users can obtain the desired output for their specific use cases.

Tokens

Tokens are the building blocks of text in large language technology and are used to determine the number of words or characters the model generates. This parameter helps users control the length of the output generated. 

Smaller LLMs support up to 1024 tokens, while larger models can support up to 2048 tokens. Establishing a token limit helps prevent irrelevant or long content.

Context Window

Context window determines how far back the LLM looks to generate the next word or prediction. The ‘window’ in this context refers to the number of preceding tokens considered for generating the next word or output in a sequence. 

For instance, a larger context window can be used if a user’s work generates summaries for large research papers or PDFs. On the other hand, a smaller context window can be used when the user is having a quick conversation with the LLM that does not require much context.

A larger context window requires more computational power, which also leads to higher processing times. Some large language models can dynamically adjust the context window, thereby providing greater flexibility while maintaining optimal computational efficiency.

Top-k and Top-p

Top-k and Top-p are techniques for managing randomness in text generation. Top-k limits word choices to a fixed number, k (e.g., the top 10 most likely words), ensuring predictable results but potentially missing subtle options.

Top-p adapts dynamically, including words, until their cumulative probability reaches p (e.g., 90 percent). It offers more flexible and contextually relevant output. Top-k is ideal for structured tasks, while Top-p is ideal for creative or dynamic tasks. These techniques can be combined to provide finer control, effectively balancing creativity and coherence.

Presence Penalty and Frequency Penalty

The presence penalty is akin to telling the model, “Stop saying the same thing repeatedly.” If you increase the presence penalty, the model avoids using words it already said, which keeps the text fresh, creative, and dynamic. Think: variety, new ideas, moving the conversation forward.

On the other hand, if you lower the presence penalty, the model uses words consistently and frequently. This can help when you are reinforcing a point or keeping things tight.

Frequency penalty has a similar range (-2.0 to 2.0), but it focuses more on how often certain words pop up. Positive values cut down on repetition. Thus, your output does not feel robotic or redundant.

Large Language Model Size

Model size refers to the total number of trainable parameters or weights that decide how the model connects the dots from input to output. Bigger models have more parameters. This allows them to pick up nuanced relationships in data and generate accurate outputs. However, larger models also incur higher costs.

Understanding and fine-tuning these large language model parameters according to your specific use cases can help you use these models effectively. Let's now examine the advantages and disadvantages of large language models.

Pros and Cons of Large Language Models

LLM agents are transforming how businesses operate, offering a host of advantages. However, as with any technology, large language models have unique challenges. 

Let us look at some of the benefits and challenges in detail.

Pros

Automation at Scale

One key advantage of large language models is their ability to automate tasks at scale. This is not only limited to content generation but also to entire datasets.

A study on automated dataset optimization with LLM agents found a 12 percent average performance boost on evaluation sets when using refined datasets for instruction tuning (Source: ResearchGate).

Brainstorming and Collaboration

Any large language model or artificial intelligence large language models cannot surpass human creativity and imagination in developing original ideas. However, we often get stuck and need inspiration for varied perspectives on a topic or problem.

Large language models may not come up with an original idea, but they help provide varied perspectives that you can use for innovative ideas. According to a study, participants who used large language model assistance generated more ideas than those who did not receive any help (Source: Arxiv).

Adaptability

Large language models quickly adapt to your preferences, doing away with the complexity of repeatedly training them. For example, if you ask ChatGPT to summarize a lengthy novella, it will generate a detailed summary requiring significant reading time. However, providing a specific input like "give a summary in bullet points that I can read in 30 seconds" will generate the output tailored to your exact requirement.

Cons

Bias: A Key Challenge for End-users

The output of large language models is based on the dataset they are trained on. This puts them at a risk of picking up inherently biased information. Imagine an AI hiring tool that starts rejecting resumes based on inherent stereotypes passed on to it through biased datasets.

For instance, iTutor Group, a tutoring business, consented to pay $365,000 in August 2023 to resolve a lawsuit filed by the US Equal Employment Opportunity Commission (EEOC). 

The company, which offers remote tutoring services to students in China, allegedly employed AI-powered recruiting software that automatically rejected male applicants aged 60 and above and female applicants aged 55 and above, according to the federal agency (Source: Reuters).

Creating Fictitious Content

Sometimes, large language models do not just get facts wrong; they even create content on topics that do not exist in the first place. For instance, a Manhattan lawyer submitted fictitious proofs to defend his client. ChatGPT generated all of this information. The lawyer had confirmed with ChatGPT about the accuracy of the cases, and it had replied that all the proofs were real (Source: Forbes).

High Costs

Developing large language models is not just a technical challenge—it is also cost-intensive. Training a model such as GPT-3 can cost around $1.4 million, requiring thousands of GPUs and substantial electricity consumption. 

Hosting these models is not economical either; maintaining a 100-billion-parameter model costs between $50,000 and $500,000 annually, and expenses skyrocket as models grow more complex. 

Anthropic's CEO predicts that training next-gen AI systems could cost $1 billion, with future iterations potentially reaching $10 billion (Deeper Insights).

As of 2024, more than half of global firms have adopted large language models such as LLama for commercial deployments. 26 percent of companies have opted for embedded models such as BERT and its variants, while just 7 percent of companies plan to incorporate multi-modal models into their operations (Source: Statista).

Future of LLMs

The future of LLMs is exciting and full of innovation. Think bigger models, smarter outputs, and tools that feel more human than machine. AI will not just create but anticipate—powering everything from personalized customer experiences to next-gen content creation.

Multimodal models that integrate text, images, and even video into a unified system are expected to gain more traction as the demand for higher automation grows. 

It is not just about being bigger and better but also about creating large language models that are efficient, accessible, and ethical. The real winners are companies that learn how to integrate these game-changing tools into their workflows.

Harnessing Full Potential of Large Language Models with Tredence

Large language models are reshaping how we create, communicate, and solve problems at scale, offering endless possibilities for innovation across industries. However, their success hinges on effectively aligning their capabilities with specific use cases while managing limitations such as biases and resource demands. 

Enterprises have moved beyond the AI hype and are harnessing its true potential to transform their business operations. Tredence plays a key role in facilitating this transformation.

Tredence combines deep data engineering, data science, and machine learning expertise with extensive domain experience to help companies modernize their data foundations and transform enterprise data into actionable insights through AI and ML.

Its portfolio includes over 140+ AI/ML accelerators designed to address even the most complex data science and machine learning challenges. These accelerators empower the largest and fastest-growing enterprises to unlock unprecedented value from their data. 

Using Tredence’s accelerators, clients can accelerate time-to-value by more than 50 percent—achieving meaningful results in weeks, not months.

Unlike outdated SaaS platforms that require lengthy implementations, Tredence data science and machine learning accelerators are customized to each enterprise's data landscape and unique business requirements. 

They reduce the total cost of ownership, capitalize on the power of hyperscalers, and safeguard your data—delivering the speed and security necessary to keep pace with today’s demanding consumers. 

Contact Tredence today and transform your organization with advanced data science and machine learning solutions.

FAQs

How many LLMs are there?

The precise number of large language models is difficult to pinpoint owing to the pace of innovation and the continuous launch of new LLMs.

Who invented large language models?

A single person or entity did not develop large language models. However, acceleration in their development is often cited as the development of the transformer architecture. OpenAI, Anthropic, Google, and Microsoft are among the companies leading with innovations.

How do LLMs work?

Large language models such as GPT, PaLM, or Llama use transformer-based architecture to learn patterns in vast text datasets. This enables them to generate human-like text and perform various language tasks through predictive analysis.

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