Introduction
Large Language Models (LLMs), like ChatGPT, represent a fascinating and rapidly evolving area of artificial intelligence (AI). These models have revolutionized the way we interact with technology, enabling machines to understand, generate, and engage in human-like conversations.
But what exactly are LLMs, how do they work, and why are they considered so groundbreaking?
This chapter explores the fundamental concepts behind LLMs, their relationship to artificial intelligence, and their practical applications. It delves into how they process and generate text, their role in generative AI, and their ability to incorporate logic and creativity.
Additionally, it highlights the transformative potential of LLMs, their current limitations, and the ethical considerations associated with their use. From addressing text hallucinations with sneaky inaccuracies to ensuring privacy and reducing biases, the guide offers insights into using and building LLMs responsibly.
Whether you’re curious about the technology powering tools like ChatGPT or interested in leveraging LLMs for tasks like translation, creativity, or recruiting automation, this overview provides a comprehensive introduction to the capabilities and challenges of this cutting-edge AI innovation.
Let’s get started!!!
Are Large Language Models (LLMs) Artificial Intelligence (AI)?
Yes, artificial intelligence is a term that encompasses all of the ways we can make a computer have logic and think and make decisions. LLMs is a technique to build artificial intelligence that can process mainly text and produce responses as text but can also create images.
What are LLMs (Large Language Models)?
If you’ve ever asked Siri a question, used Google Translate, or chatted with an AI assistant, you’ve already experienced LLMs! But what exactly are these mysterious “Large Language Models”?
In the simplest terms:
- LLMs are a type of AI that can read, write, and understand human language
- They are trained on massive amounts of text data (think of everything from books and articles to social media posts).
- This training helps them learn patterns, words, and sentences so they can answer questions, write essays, or even have conversations like a human would. Imagine a very smart parrot that’s read almost every book in the world. When you ask it something, it can respond based on all the information it’s read! The resemblance to a parrot is a very important one to help us understand the capabilities and limitations of this technology.
How Do LLMs Work? And are LLMs the same as Generative AI (GenAI?)
Let’s break this down with an analogy of how things work:
The Brain of an LLM: Think of an LLM like a giant “library brain.” It doesn’t memorize every single book, but it remembers the patterns and ideas from all the books it’s read.
Learning the Language: When an LLM reads through text data, it learns which words go together, how sentences are structured, and what different phrases mean. It’s like a child learning a language by listening to adults talk.
Generating Responses: When you ask an LLM a question, it uses what it’s learned to predict the best response. It’s not thinking like a human, but it’s making a very good guess based on patterns it’s seen before. They can also create new content, like writing stories, making poetry, or even helping with coding. This is why they’re called “generative AI” – they can generate new things, not just repeat what they’ve seen before.LLMs are the powerhouse of Generative AI.
Some names of LLMs that are Generative AIs are ChatGPT (OpenAI), Gemini (Google), LLama (Facebook), Claude (Anthropic), and others.
Does current AI have memory? Can it learn and evolve by interacting with us?
Yes, current LLMs can create a database that memorizes prior interactions. It’s like having a friend who gets to know you better over time. For example, they remember how you like your coffee and suggest activities you might enjoy. Or remember what your writing style is, and can default to writing the way you want them to.
Why Are LLMs a Big Deal?
-They Save Time: Need a quick answer to a question or to automate a repetitive task? LLMs can help instantly.
-They Break Language Barriers: With translation capabilities, LLMs make it easier for people from different cultures to communicate.
-They Assist in Creativity: Whether you’re writing a novel, composing a song, or designing a new app, LLMs can help you brainstorm and bring your ideas to life.
–They can incorporate logic: When you built LLMs a certain way they can incorporate logic in their answers that can help you automate complex tasks
What Should We Be Careful About When Using LLMs?
As exciting as LLMs are, there are a few things to watch out for:
- They’re Not Always Right: Sometimes LLMs might give incorrect or misleading answers because they don’t truly “understand” the world like humans do. They just make predictions based on patterns. This is called “hallucinations” and they can be misleading and very difficult to predict. There are cases where people have used LLMs for legal cases and have gotten responses with hallucinations that are very difficult to verify even for legal experts.
- Bias in Responses: If the text data used to train an LLM has biases (like stereotypes or incorrect information), the AI might reflect those biases. It’s like a child picking up bad habits from the wrong influences. A lot of effort is put into practice to make sure that the LLM outputs are not inherently biased and that the training content does not contain inappropriate inputs or that the generated content is inappropriate.
- Privacy Concerns: LLMs can remember patterns from the data they’ve seen, so it’s important to use them responsibly and protect sensitive information. This is very important for mission-critical use cases e.g. in law or medicine where LLMs can be forced to output personally identifiable information when they were trained with this data and get asked a certain way for this output. Privacy when building LLMs for data sensitive use cases is a very important aspect for engineering purposes.
How can we avoid hallucinations of LLMs?
There are many proposed ways to avoid getting wrong hallucinated responses from LLMs. The best way to achieve this is with the use of multiple LLM Agents. An agent is an LLM that is trained to behave like an expert only in a specific part of the task. Many agents can collaborate with each other and produce output that cannot be achieved otherwise with a single non- expert LLM.
Another way to avoid hallucinations is to use the so-called Retrieval Augmented Generation (RAG). In simple terms, RAG is intended to force the LLM to use only a specific corpus of text when generating responses to avoid imagining any parrot- like content from its large memory of texts.
The best way to avoid hallucinations when developing applications with LLMs is to create a test for your LLM tasks. Specific inputs should be creating specific outputs. For example, if you pass the same CV to an LLM the report output and score should always be the same. Whenever you change any parameter while developing this technology the test should be passed again. This is called Prompt Driven Test Development and is becoming the most important part of developing applications with LLMs that are consistent and safe.
What can LLMs do well today?
When engineered the right way with tests for consistency and special techniques to avoid hallucinations LLMs can perform well in specialized tasks that are repetitive and mundane for a human performing them. They cannot be used out of the box e.g. take a ChatGPT and use it to make diagnoses. With special engineering though they can be very effective even in relatively complex tasks.
The Future of LLMs
The technology behind LLMs is improving every day. When built by skilled people aware of the limitations and with the right guardrails we can currently build very smart assistants that take relatively complex and repetitive tasks and automate them.
We are not yet to the point of having a medical doctor or a lawyer in our pocket.
My personal opinion is that LLMs will be used to take menial, repetitive tasks that require noncomplex logic away from our everyday jobs and elevate us to focus more on human touch and management. For example in recruiting by taking away all
the repetitive tasks we can have more time to interact with candidates and build trust with them to convert them to applicants.
Final Thoughts
LLMs are like supercharged language tools. They’re changing how we interact with technology and opening up exciting new possibilities. Whether you’re using them to write, chat, or learn, they’re here to make our lives a bit easier – as long as we remember that they’re still learning, just like us.
In a nutshell: LLMs are like really smart parrots that have read a lot of books. They can help us with almost anything involving language, and they’re getting better all the time!
So the next time you talk to an AI, remember – you’re having a conversation with one of the coolest and most complex technologies out there. 🧠🤖💬 Just be aware that they are just parrots and if you want to build a complex technology with those you need to avoid parrot-like hallucinations and build guardrails to make them behave predictably.
