Exploring the Evolution of Large Language Models: From ELIZA to GPT-4

Isaac Dev and Devops
5 min readJan 11, 2024

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Hey there! Let’s take a dive into the world of natural language processing, a field that’s been buzzing with excitement thanks to some pretty cool neural network models. We’re talking about the big names like BERT, GPT-3, and their siblings that have been changing the game in how computers understand and play with language.

But wait, let’s rewind a bit. These awesome LLMs (aka large language models) didn’t just appear out of thin air. They have a backstory that’s worth a peek if we really want to get what’s going on today.

The Early Days — ELIZA and SHRDLU (The OG Chatbots)

First up, let’s talk about ELIZA, a creation of the 60s at MIT. Picture this: a computer program that chats with you, kinda like a therapist. ELIZA used a bunch of pattern matching tricks to turn your words around and ask you more questions. Pretty clever, huh? But here’s the catch — ELIZA was more like a parrot. It didn’t really ‘get’ what you were saying. It had a set of pre-cooked responses and didn’t remember a thing you said a minute ago.

Then there’s SHRDLU (also from MIT, in the 70s). This one was a bit more brainy. It understood full sentences, had a make-believe world it could chat about, and could even clear up some confusion in what you asked. Think of it as a more sophisticated chatbot that could keep up a bit better with the conversation.

Now, ELIZA was limited by the technology of the time, but it pioneered a new approach to human-computer interaction. Though simple, ELIZA impressed many people by appearing to understand conversational language. It inspired decades of AI research into natural language processing and conversational agents.

SHRDLU went further by representing simplified blocks worlds that it could manipulate through natural language commands. It demonstrated how AI systems could parse language, represent knowledge, and reason about domains. SHRDLU laid the groundwork for expert systems and knowledge representation in AI.

Ultimately, these early chatbots were stepping stones toward the goal of creating AI that can truly understand natural language. They were limited by the available data and computing power of their time. Still, ELIZA and SHRDLU proved conversational AI was possible and paved the way for future breakthroughs.

The Rise of Statistical and Neural Language Models

So fast forward to the 80s and 90s, and things started to get statistical. Researchers were playing around with n-gram models, which are like educated guesses on what word might come next in a sentence, based on tons of data. Still pretty basic, but hey, it was a step up.

The 2000s were a game-changer. With more data and better computers, neural networks entered the chat. These are like digital brains that can learn patterns. Long Short Term Memory networks and stuff like gated recurrent units (think of them as memory cells) helped models get a grip on context and memory.

Basically, statistical NLP allowed AI systems to analyze huge datasets and derive probabilistic rules for generating and understanding language. This enabled more complex tasks like machine translation. However, these methods relied heavily on surface-level statistical relationships between words.

THEN, Neural networks revolutionized NLP by learning deeper linguistic patterns from large corpora. Word embeddings captured semantic meaning, and sequence models handled long-range dependencies. Neural networks outperformed traditional NLP techniques by learning powerful representations from data.

Together, statistical and neural language modeling marked a shift toward a lot more “big” data-driven techniques in NLP. By combining robust datasets with more sophisticated learning algorithms, AI systems could continually improve at human-like language use.

Transformers Unleashed — GPT-3 and Beyond

Then, bam! 2017 brought us transformers (no, not the robots). These are super smart at handling sequences of data (like sentences). They look at words in context from both directions — not just front to back. This was big news for language understanding.

GPT-3, which landed in 2020 courtesy of OpenAI, was a total showstopper. With a whopping 175 billion parameters (think of these as dials and knobs that the AI can tweak to get smarter), it could churn out text that was eerily human-like. It was good at picking up new tricks quickly (like translating languages) and crafting content that blew people’s minds. But it was also a bit of a resource hog, costing a pretty penny to train and run.

In-short GPT-3 amazed people with its versatility and strong language generation abilities. However, it also demonstrated some of the limitations of current LLMs. Without explicit training in reasoning, GPT-3 often halts logical consistency and factual accuracy, and the term “hallucination” basically became mainstream. GPTs generate language statistically… which means it can often generate (i.e. make-up) facts and misinformation, too.

Because of that, researchers are now exploring ways to improve reasoning, semantic understanding, and knowledge representation in LLMs. Approaches like self-supervised learning over structured knowledge bases may endow models with better relational knowledge and common sense. So as models grow larger, training them efficiently also becomes critical (because it can often cost 6 to 8 figures USD). Methods like transfer learning, meta-learning, and multi-task learning allow models to build on existing knowledge rather than training from scratch — helping to make giant models more practical, even on consumer grade hardware (hello personalized mini-GPTs!)

The Next Frontier — GPT-5 and Beyond

Looking ahead, we’re all waiting for GPT-5, expected to hit the scene in 2024. What’s it going to bring? Probably more parameters, deeper neural networks, and hopefully, better “smarts” when it comes to facts, reasoning, and even a bit of common sense. That’s all a given. But the focus seems to be less on that, and more on multi-modal models, which can juggle different types of data like images, videos, and text. We’ve already seen DALL-E being incorporated pretty seemlessly into chatGPT to generate images on the fly, but there’s still more road to travel. With advancements in 3D modeling AI and text-to-video generation, we’re talking about models that not only talk the talk but can walk the walk too, understanding and generating ideas in a way that feels more human in more than just text and image format.

There are also growing concerns about the societal impacts of ever-more-capable LLMs. Issues like bias, misinformation, and malicious use need to be proactively addressed through technical solutions and policies

So as these models grow in scale and complexity, it’s super important to keep things responsible and ethical. But man, the progress so far is pretty exciting! We’re looking at a future where AI could change how we live and work in ways we’re just beginning to imagine. Stay tuned — the best is probably yet to come!

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