Foundations of LLMs 1943–2026: A Curated Collection

The curated collection “The Foundations of LLMs 1943–2026” follows one of the most interesting conceptual journeys in contemporary science, from a 1943 study about artificial neurons to the huge language systems that run on your laptop today. And to be honest? Researchers who have too much time on their hands don’t only want to know this lineage for fun. It’s really helpful for anyone who uses AI tools like ChatGPT, Claude, or DeepSeek.

Every modern Large Language Model is built on decades of accomplishments that have built on each other. Mathematicians, neuroscientists, and computer scientists all played important roles. They often didn’t know how their work will all go together in the end. This carefully chosen group of artifacts tells a clear story. You’ll learn how an article from 1943 on fake neurons led to GPT-4. When I first traced the connection correctly, it startled me.

Why This Curated Collection Matters

Most individuals see LLMs as finished goods. They type a question, get an answer, and then go on. But the architecture behind that answer goes back eighty years. It uses ideas from computational theory, linear algebra, and probability in ways that still affect everything you read today.

Why do you need to know about history? Because knowing how things work helps you use these tools better. Understanding how attention mechanisms work helps you understand why prompt engineering is important. Understanding tokenization also helps explain why LLMs have trouble with some math issues. It explains why they’ll confidently get something wrong that a calculator can do in a few hundredths of a second. For example, if you ask an LLM to count how many times the letter “r” appears in “strawberry,” it will often give you the inaccurate answer. This isn’t because the model is negligent; it’s because it only sees tokens, not individual letters. That’s a direct result of how tokenization works, and knowing that impacts how you think about tasks.

For years, I’ve been reading these studies and following these links. What keeps surprising me is how unclear the route was. Progress wasn’t straight at all. There were dead ends, AI winters, and comebacks that no one saw coming.

The curated collection of LLMs 1943–2026 puts these breakthroughs in a coherent order. It talks about the people, publications, and ideas that made modern AI feasible. It also demonstrates that the researchers who developed each layer didn’t always know what the next layer would look like.

Here’s a short look at the most important times:

Each era built directly on the last. Consequently, you can’t fully understand transformers without grasping backpropagation first. That’s not gatekeeping — it’s just how the dependency chain actually works.

From Turing to Transformers: The Math

The plot starts in 1943. Walter Pitts and Warren McCulloch wrote “A Logical Calculus of the Ideas Immanent in Nervous Activity”. This research suggested that neurons might be represented as basic logic gates. It was the first time that biology and computation were linked. That was a truly revolutionary notion, yet most people today have never heard of it.

Alan Turing’s contribution came much earlier, in the form of his 1936 article on computable numbers. His idea of a universal machine showed that computation may be made more formal. In addition, his 1950 study “Computing Machinery and Intelligence” wondered if robots could think. That question is still the main focus of AI research every day, even after 80 years.

Frank Rosenblatt came up with the Perceptron in 1958. It was the first neural network that could be trained. It could learn how to sort things into groups. But in 1969, Marvin Minsky and Seymour Papert showed how limited it was, starting the first AI winter. For more than ten years, progress stopped. Their criticism was clear: a single-layer perceptron can’t learn any function that isn’t linearly separable, hence it can’t solve the XOR problem. That sounds small, but it was enough to take away money and interest from the whole field for years. That should sound very familiar if you’ve been following the hype cycles around AI lately.

Everything changed when backpropagation came along. David Rumelhart, Geoffrey Hinton, and Ronald Williams made the idea public in 1986, even though it had been around in several versions before. Backpropagation helped multi-layer networks learn by figuring out what went wrong at the output. Then it sent those mistakes back through each tier. Each weight is changed in proportion, and this is still how neural networks learn today. That’s a long time to stay strong, forty years.

The chain rule from calculus is what makes backpropagation work. More specifically, it finds partial derivatives through each layer. Gradient descent, on the other hand, employs such derivatives to cut down on mistakes. These ideas are some of the most important parts of the LLMs 1943–2026 curated collection. If you’re new to calculus, be warned: the learning curve is genuine. One method to get a feel for things before getting into the formalism is to work through a small two-layer network by hand. First, do a forward pass, then calculate the loss, and last, trace the gradient back by hand. It takes a lot of time, but doing it once makes the abstract apparatus real in a way that reading never truly does.

Some important building blocks of math are:

• Linear algebra — matrix multiplication powers every neural network layer
• Probability theory — softmax functions convert raw outputs into usable probabilities
• Information theory — cross-entropy loss measures how badly the model is predicting
• Calculus — gradients guide the entire learning process
• Statistics — Bayesian methods inform how language modeling approaches uncertainty

Attention Mechanisms and Transformer Architecture

The transformer revolutionized the way natural language processing works in every way. “Attention Is All You Need” by Vaswani et al. came out in 2017 and offered a completely new architecture. It completely gave up on recurrence. Instead, it just used attention processes, and the field has never looked back since.

What is attention, really? In simple terms, it enables a model focus on the portions of the input that matter when it makes an output. Think about reading a long paragraph. When you try to understand the last statement, your brain doesn’t weigh each word the same way. Attention functions the same way in neural networks as it does in other systems. To be honest, it’s more straightforward than most explanations make it sound. Think about this sentence: “The trophy didn’t fit in the suitcase because it was too big.” A person reading this would naturally link “it” back to “trophy” instead of “suitcase.” The Query vector for “it” scores highest against the Key vector for “trophy,” and that relationship is stored in the output. A well-trained attention mechanism achieves the same thing.

But the idea didn’t just come out of nowhere. In 2014, Bahdanau, Cho, and Bengio came up with the idea of attention for machine translation. They proved that fixed-length encodings were losing important data. Luong et al. also improved the method in 2015. Both predecessors are important parts of the LLMs 1943–2026 curated collection’s basis. If you want the whole story, you should read both.

The transformer’s main new idea is self-attention. This is how it works in simple terms:

1. Each word in a sentence has three vectors: Query, Key, and Value.
2. The model figures out how similar all the Query-Key pairs are.
3. The ratings tell each word how much it “attends to” every other word.
4. The final result is a weighted sum of the Value vectors.
5. This happens at the same time on more than one “head.” This is called multi-head attention.

The arithmetic is beautiful: Attention(Q, K, V) = softmax(QK^T / √d_k)V. The √d_k division keeps the dot products from getting too big. As a result, gradients don’t change during training. That problem was always a difficulty for earlier architectures.

One essential trade-off to note is that attention is powerful but costly. The cost goes up by a factor of two for every pair of tokens when you compute attention scores. For an input of 1,000 tokens, it takes about a million score calculations; for an input of 10,000 tokens, it takes about a hundred million. This is why it has taken a lot of engineering work to make context windows bigger, from 4K tokens to 128K and beyond. Some of the strategies used include sparse attention, sliding-window attention, and rotary positional embeddings. Knowing about this trade-off helps us understand why longer context windows cost more and why model suppliers charge more for them.

Why transformers are better than older architectures:

• Parallelization: Unlike RNNs, transformers process all tokens at once, which speeds up training by a lot.
• Long-range dependencies: attention connects words that are far apart without the problems that RNNs had with information decay.
• Scalability: performance becomes better as more data and parameters are added.
• Flexibility: the same architecture may be used for translation, summary, generation, and more.

Recurrent Neural Networks and Long Short-Term Memory networks were the most important types of NLP before transformers. But they only worked on sequences one token at a time. That was slow and likely to forget early inputs in long sequences. The transformer fixed both problems at the same time. So, it became the main part of every big LLM. Over the years, I’ve seen a lot of changes in architecture, but this one really made a difference.

From BERT to GPT-4: The Modern LLM Era

The transformer paper let the floodgates open. Two important models came out within a year. They built on the same base but went in quite different paths.

In 2018, Google came out with BERT and OpenAI came out with GPT-1. BERT used bidirectional training, which meant that it looked at context from both sides at the same time. That makes it great for figuring out how to do things like search and sort. GPT, on the other hand, used left-to-right instruction, which helped it write better. That bifurcation in the architecture still characterizes the field today. A good way to demonstrate this distinction in action is to ask a BERT-based system to fill in a missing word in a phrase. It does a good job because it can see the whole context around the word. It has trouble writing the next three paragraphs of a story since it was never taught to do so in an autoregressive way. Models like GPT have the opposite profile.

The roots of LLMs 1943 2026 curated collection show how these two methods separated and changed over time:

• 2018: BERT and GPT-1 show that transformer pre-training works on a large scale.
• 2019: GPT-2 shows that scaling makes quality much better (and triggers the first serious AI safety panic).
• 2020: GPT-3 learns with only a few examples and has 175 billion parameters.
• 2022: ChatGPT makes LLMs available to a lot of people practically right away.
• 2023: GPT-4 adds multimodal features, and Claude 2 focuses on safety alignment.
• 2024: DeepSeek and open-source models start to significantly challenge proprietary dominance.
• 2025–2026: Mixture-of-experts, longer context windows, and reasoning chains push the limits even farther.

Every stage was based on the same transformer. Three things led to improvements: more data, more parameters, and better ways to learn. The scaling hypothesis, which says that growing things bigger would always make them smarter, worked better than expected. Almost too much.

The truth is, the differences in architecture between the top LLMs are really important. Anthropic made Claude, which focuses on constitutional AI and safety alignment. ChatGPT learns how to act by using reinforcement learning from feedback from people (RLHF). DeepSeek leverages a combination of professionals to get things done faster and for less money. People say that its training cost a small fraction of what similar Western models did. They have transformer DNA in common, but their training methods are very different. Those differences show up in the actual results. If you run the same morally unclear situation through Claude and ChatGPT, you’ll often receive quite different answers. This isn’t because one is wiser; it’s because they were trained to look for different things. That’s a direct result of the different ways of training, and knowing this will help you pick the proper tool instead of just the most popular one.

To understand the changes, you need to know the basics of the LLMs 1943-2026 selected collection. You can’t really decide which model is best for you without understanding how the architecture works. On the other hand, knowing the basics lets you guess where these models will get better and where they will remain having problems.

Connecting History to Practical AI Use

Theory is important. But how you use it is more important. So, how does knowing the basics of the LLMs 1943-2026 selected collection benefit you in your daily life?

Better engineering of prompts. Tokens, not words, are what transformers work with. This is why “Explain quantum computing” and “Quantum computing: explain simply” give different answers. The attention mechanism gives varied weights to tokens depending on where they are and what they mean. So, the structure of the prompt has a direct effect on the quality of the result. You can learn to predict and use how it changes quality in certain ways. A real-world example: if you want a model to summarize a long document, putting your explicit instructions at the beginning and end of the prompt, instead of just at the top, takes advantage of the model’s tendency to give more weight to early and late tokens. That’s not a hack; it’s just how positional encoding and attention work together.

Choosing models that are smarter. It’s not true that all LLMs are good at everything; the disparities are not random. BERT-style models are still the best for search and categorization. Models like GPT are great at generating. When it comes to translation, encoder-decoder models are the best. I have tried out dozens of task-model combinations, and this framework works. If you know about architecture, you can make better choices instead of just going with what’s popular.

Finding and fixing problems in AI outputs. An LLM isn’t “lying” when it hallucinates. It’s making the next token that is most likely to happen based on what it has learned. This information will help you make better guardrails. It also explains why retrieval-augmented generation (RAG) works so effectively to cut down on hallucinations. You’re tying that probability distribution to real source material. For example, a basic GPT-style model that wasn’t trained on a specific rule can confidently come up with plausible-sounding but made-up details when asked about it. Instead, the identical model with a RAG pipeline that pulls the actual regulatory language will correctly cite it. The generation mechanism is the same; what changed is the information the attention mechanism gets to work with.

Here are some useful strategies for putting this information to use:

1. Learn the foundations of tokenization. Tools like OpenAI’s tiktoken show you exactly how models see your text, which is often surprising.
2. Know what context windows are. Longer isn’t necessarily better; attention costs go up by the square of the sequence length, which can grow expensive very quickly.
3. Learn the difference between fine-tuning and prompting. Sometimes a smaller, more fine-tuned model beats a big, general one, and knowing when can save you a lot of money.
4. Keep an eye on the open-source space. Models like Llama and Mistral are making things easier to get to in important ways.
5. Keep up with the research—papers on arXiv today turn become products tomorrow, and the time between them is increasing shorter every year.

The curated collection of LLMs 1943–2026 isn’t just a history book; it’s a guide. In particular, it shows patterns that can help us guess what will happen next. Scientists are already looking into other options outside the usual transformer. State-space models like Mamba threaten attention’s supremacy by delivering linear scaling with sequence length instead of quadratic scaling. Still, attention-based designs are the best option for now. That might change. But it will take something very interesting to break the momentum that has been building for eighty years.

Conclusion

The curated collection of LLMs 1943–2026 recounts the story of a series of innovations that built on each other. Each one opened up the next one, often decades later and in ways that no one saw coming. Understanding this history changes you from a passive AI user to an informed practitioner, from McCulloch-Pitts neurons to GPT-4. That difference is more important than ever right now.

So here are the things you may take right away. First, read the original paper “Attention Is All You Need.” It’s surprisingly easy to read, especially if you don’t know much math. Second, try out different tokenizers to observe how your language is actually processed by models. Third, give Claude, ChatGPT, and DeepSeek the same prompts. See how changes in architecture and training lead to results that are very different. In addition, save the foundations of LLMs 1943-2026 curated collection as a living reference. When new ideas come up, go back to it. They will always come up. In the end, the only way to know where AI is headed is to know where it has previously been.

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