☕ Decoding Language: Brewing Language Models from BERT to GPT!

📚 Table of Contents

• From RNN to Transformer: The Old Recipes of NLP
• 🧠 LSTM: Long Memory Language Master
• 🔄 GRU: Fewer Gates, Faster Learning
• ⚡ Transformer: Attention is Everything!
• 🧩 BERT: Transformer Deep in Context
• ✍️ GPT: Master of Text Production
• 🔁 T5: The Model That Converts Everything to Text 

Artificial intelligence insights shed light on the breakthroughs that make AI feel more conversational and human-like than ever before. AI has become like our best friend—chatting with it is now as natural as sipping our morning coffee. In this article, we will explore how artificial intelligence deciphers language, why models like BERT are important, and the journey that leads to today’s giant LLMs (Large Language Models).

**The image was produced with Microsoft Bing Image.

From RNN to Transformer: The Old Recipes of NLP

First, let's start brewing with recurrent neural networks (RNN). RNN are neural network created to work with sequential data such as text, audio, video, etc. Its working logic is simply as follows: it receives data in the input layer. It preserves information in the hidden layer. It produces output appropriate to the data in the output layer. Since it has limited memory, it has difficulty in capturing long-range dependencies. To overcome this problem, improved structures such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) emerged; these models were able to preserve the flow of information by remembering long-term dependencies better.

🧠 LSTM: Long Memory Language Master

LSTM models are RNN derivatives that can hold information for a long time, and they achieve this with three basic components: the input gate, the forget gate, and the output gate. The input gate decides what information will be taken into the cell; the forget gate decides what will be deleted; and the output gate decides what information will be transferred out. These gates control the flow of information by working with activation functions such as sigmoid and tanh. Thus, LSTMs create a stronger memory structure by remembering important information from previous steps.

🔄 GRU: Fewer Gates, Faster Learning

GRU (Gated Recurrent Unit) is a type of RNN developed to work with sequential data, similar to LSTM, but its structure is simpler. It uses only two gates (update and reset gates) instead of three gates in LSTM. In this way, both training time is shortened and similar successes can be achieved with less computational resources. GRUs provide fast and effective solutions, especially in tasks with short- and medium-term dependencies.

⚡ Transformer: Attention is Everything!

The Transformer model was introduced in the article “Attention Is All You Need” published in 2017. This model eliminates the need for RNNs to process words sequentially, giving them the ability to evaluate all words simultaneously (parallelism). Transformer models train faster. They also provide more robust results on long texts. This architecture forms the basis of all major language models (LLMs) used today.

🧩 BERT: Transformer Deep in Context

The BERT (Bidirectional Encoder Representations from Transformers) model was developed by Google in 2018. This model has achieved great success especially in tasks such as classification, semantic search and question-answer. BERT has become a milestone in many NLP tasks with its “pre-training, then fine-tuning” approach.

✍️ GPT: Master of Text Production

GPT (Generative Pre-trained Transformer) was developed by OpenAI. It is a powerful model for text production, story writing, and dialogue-based applications. As GPT models (GPT-2, GPT-3, GPT-4…) grew, they began to produce much more creative and contextual texts. GPT represents the creative side of LLMs.

🔁 T5: The Model That Converts Everything to Text

T5 (Text-to-Text Transfer Transformer) was developed by Google. This model treats NLP tasks as “text-to-text transformations”. For example, tasks such as question-answer, translation, and summarization are all modeled as “Input text → Desired output text”. Thanks to this flexibility, T5 has become a powerful and general-purpose model that performs well in a wide variety of tasks.

This journey from RNN to Transformer, from BERT to GPT in the world of artificial intelligence; has given us a completely different perspective on how language is understood, processed and produced. Each model was shaped according to the needs of its time and laid the foundations of the huge language models we use today. If you want to discover these technologies together and follow the new generation artificial intelligence developments closely, don't forget to subscribe to my blog. How about "brewing languages" together? ☕🧠