Large Language Models (LLM): A Deep Dive

Large language models are a true tech discovery of the 2020s. Although AI technologies have been around for over 60 years, they have only now made some noise among people thanks to the public release of such LLMs as ChatGPT and Llama. Now, with such powerful features in hand, people understand how capable artificial intelligence is and how LLMs can change the world (and are already doing it).

However, how much do people really know about LLMs? Not every person will be able to correctly name the principles of this popular technology. In this LLM deep dive​, we will tell you about the main characteristics of LLMs and how your business can take advantage of them.

What are Large Language Models?

Let's start with the simplest. A large language model is a type of neural network that can analyze and understand human language and provide meaningful, coherent, and fairly accurate textual responses to user requests.

Large language models have become an effective tool for business almost immediately. They can take on the automation of repetitive tasks, communication with clients within the custom service chatbot, content creation and editing, data analysis and insights, and much, much more.

Now, let's look at LLM from a more technical side to understand how this technology works and how exactly to get the most benefit from it.

The architecture of an LLM: Key components explained

The LLM’s architecture is built on deep learning principles and usually uses transformer-based neural networks. Here are the key components:

Tokenization

This process converts text into smaller parts—tokens—like words and/or syllables. Usually, it’s done with the help of Byte Pair Encoding (BPE) or WordPiece models. Then, the final tokens are mapped to unique numerical representations before further processing.

Embedding layer

When the text is broken down, the model gives each one a unique ID and assigns them a position in a giant map of meaning. It’s done so that the model can understand the semantic relationships between words. For example, "king" and "queen" have similar embeddings. Usually, modern LLMs learn all these embeddings from scratch, but there are pre-trained embeddings that can be used to make the learning process faster.

Transformer architecture

This is the heart of any large language model. All the magic happens here: Instead of reading words one by one, the model looks at everything at once. Then it learns the relationships between words and weighs them depending on the overall context. The key components include:

• Self-attention mechanism (scaled dot-product attention)

• Multi-head attention (to capture different characteristics of word relationships)

• Feedforward neural networks (non-linear transformations to refine representations)

• Layer normalization and residual connections (to prevent information loss between layers)

Positional encoding

Since transformers don’t know the correct order of words in a sentence, positional encoding gives it tiny clues on what the order should be. Without it, the model won’t learn how to put words in a sentence correctly and won’t be able to accurately remember it.

Decoder

This part is responsible for the way LLM answers your questions. With the help of a decoder, a generative LLM can predict the next word in its response based on everything it learned before. That’s how “talking” with an LLM becomes possible.

Training

Here, the learning process starts. First of all, the model consumes all the data you have prepared for it. It can include books, newspapers, web pages, emails—everything that contains human text. This is how it learns how to correctly formulate sentences and predict next words in its responses. When the training is done, people fine-tune the model to improve its performance and/or teach it to complete more industry specific tasks (we’ll talk more about it further).

Memory and context window

Unfortunately, the model can’t “remember” everything forever. Usually, things that the model can hold in its memory during one interaction is limited by the number of tokens—a context window. For example, ChatGPT has a 32,000 token context window.

Output layer

This is basically what you are talking with when you write your questions into a chat window. During this process, the model converts existing responses (context) into probabilities for the next token. Depending on how you want your answers to be written, it can play the safe mode and choose the most probable token or be a bit more creative.

Fine-tuning LLMs: How to customize AI for your business needs

As promised, more details about fine-tuning. Usually, a generally-trained LLM knows a little bit of everything at the same time, without any deep knowledge of specific industries. Fine-tuning is the process of feeding the model more specialised data so it can perform your business tasks better. The data will depend on your industry. For example, if you work with finances, you will have to “show” the model modern financial laws, invoices, financial reports, and banking terms.

Feeding the model more specific data is only a part of fine-tuning. The next step is adjusting the system’s responses. Humans review the way the model responds to queries, correct any mistakes that slip into the answers, and polish the final results to perfection. The goal is to make the model as precise and accurate as possible.

The developers can also apply Reinforcement Learning from Human Feedback (RLHF) to align the model’s responses with what users expect from them. On this stage, humans rank the answers depending on how “good” or “bad” they were and fix bias for more neutral results.

“But why go through all this if I can just write better prompts?” Great question. Prompt engineering is an effective way of getting most LLMs to . But if you need consistent domain-specific results without spending too much time writing and iterating the prompts, you need to fine-tune your model.

Evaluating LLM performance: Key metrics and benchmarks

Well, the model is fully trained. Now what? Now we move onto measuring the LLM’s performance. Usually, this process includes the following aspects:

Language understanding

This one is self-explanatory. You need to see how accurately your model understands the questions it’s asked and how often it makes mistakes. The metrics here are:

• Perplexity (PPL): Measures how "surprised" the model is by new text. Lower is better.

• BLEU (Bilingual Evaluation Understudy): Compares generated text to reference texts (good for translations).

• METEOR (Metric for Evaluation of Translation with Explicit ORdering): Similar to BLEU but considers synonyms and word order.

• ROUGE (Recall-Oriented Understudy for Gisting Evaluation): Measures how much of a reference summary is captured.

Logical consistency

You should evaluate the logical reasoning behind the LLM’s responses. The examples of benchmarks for this part include:

• TruthfulQA (avoiding misinformation)

• HellaSwag (picking the most logical next sentence in a story)

• MMLU (knowledge across industries)

Speed

Another important metric is the speed at which the model gives its users the answers. It’s usually measured with Tokens Per Second (TPS), latency, and throughput. The faster your model can generate correct answers, the better.

Fairness

Even with all the human testing and feedback, the model can still have bias and discriminate against some viewpoints. You need to make sure that it doesn’t happen and your model acts as neutral as possible. You can use

• Bias Benchmark for QA (BBQ) for bias across different social categories.

• Winogender/WinoBias for possible gender discrimination.

• CEAT (Contextualized Embedding Association Test) for biased word embeddings.

Security

Cybersecurity is an absolute must in the modern tech-based world. Plenty of software uses and stores personal info that needs utter protection from criminals. AI is no exception, especially if you plan to use your LLM for handling sensitive information like legal and medical cases. How does the LLM treat weird or malicious inputs? You can find it out with the following tools:

• AdversarialQA (tests how easily the model is tricked by confusing questions).

• Trojan Detection Benchmarks (check if a model can be exploited with hidden triggers).

• ToxiGen (evaluates whether the model generates toxic or harmful language).

Task-specific performance

If your model is already fine-tuned for your business tasks, you can test it for them. You can use specific tools like MedQA-USMLE for medical licensing exam questions or CaseHOLD for legal document summaries. Depending on your industry, you can find other tools that are suitable for your solution.

Hallucinations in LLMs: Why they happen and how to reduce them

Hallucinations in a Large Language Model (LLM) happen when the model generates false, incorrect, or even made-up information that sounds like a fact. And since they don’t really “know” a lot and just predict the next words based on the data they were trained on, such mistakes can happen quite easily. There are plenty of factors contributing to this: 

• No real-world verification and fact-checking.

• Overgeneralization and creating new “facts” by using real facts as patterns.

• Data limitations and no real-time awareness.

• Unclear and vague prompts.

• Complex reasoning limitations.

LLMs can generate fake citations and references, historical events that never happened, made-up quotes, and other types of false information. It can be extremely dangerous in industries like healthcare and finances that heavily rely on accurate facts. And even if your business is not as data-centered, you should still be aware of the fact that your LLM can “lie” to its users.

You can minimize the risk of hallucinations by applying fact-checking tools, Retrieval-Augmented Generation (RAG), and human-in-the-loop verification.

Memory and context in LLMs: How AI ‘remembers’ information

Unlike humans, LLMs don’t have long-term memory. Instead, they rely on context windows to "recall" recent information within a conversation/task. A context window is the amount of tokens that the model can “remember” during one interaction. If a conversation goes beyond the set limit, older information is "forgotten." For example, you can mention your name in the beginning of your chat with the LLM and it can even call you by it in its responses, but if your dialogue continues long enough, it will lose this piece of information.

Context windows help LLMs support the illusion of “memory-like” behavior. You can repeat key information in prompts to make sure that the most important stuff stays in the system. Another approach is fine-tuning LLMs on relevant information, so they respond more consistently.

LLMs don’t store personal user interactions all the time for various reasons. Privacy, scalability, costs, resources. However, researchers are working on memory-enhanced AI that:

• Stores persistent user preferences (like a personal assistant).

• Retrieves and updates knowledge over time without retraining.

• Balances privacy with personalization.

Bottom line

Large Language Models are an extremely useful tool for any business. They can easily automate processes, improve customer service, and help with decision-making and research. The number of tasks they can facilitate is truly amazing. However, going into this integration blind is a risky move. If you don’t know how it works, you won’t be able to uncover its full potential. But now that you know what’s going on under the hood of an LLM, this AI technology can level up your business.

If you want to integrate an LLM into your business processes, contact us! Yellow is an AI software development agency that is ready to turn your idea into reality.