# introduction
large language model (LLM) may seem complicated at first. There are transformers, attention layers, scaling laws, pre-training, instruction tuning, human feedback, search, and many other ideas around them. But the best way to understand large-scale language models is not to start with a giant textbook. A better method is to read some important papers that describe the main parts of the system.. This article is part of a fun series where you learn by exploring the core ideas, hands-on projects, and research papers behind the latest technologies. In this article: 5 papers explaining how LLM works. So let’s get started.
# 1. All you need is attention
this is, All you need is attention paper introducing transformer architecturewhich is the basis of the modern LLM. Before Transformers, many language models used recursive or convolutional architectures to process sequences. This paper showed that powerful sequence models can be built using attention alone. The most important concept in this paper is self-attention. Self-attention allows each token in the sequence to look at the other tokens and decide which ones are most important. This is one of the reasons why LLMs are able to understand the context of long sentences and entire paragraphs. This paper also introduces multihead attention, positional encoding, and the general Transformer block structure. This is important because almost all major LLMs today are built on Transformer ideas, including GPT, Llama, Claude, Gemini, and Qwen style models.
# 2. Language models can be trained in a small number of times
this is, GPT-3 paper. This describes one of the biggest changes in natural language processing (NLP). Instead of training a separate model for each task, you can simply read the instructions and examples in the prompts to enable large language models to perform many tasks. In this paper, we introduce GPT-3, a 175 billion parameter autoregressive language model trained to predict the next token. What is most interesting is not only the size of the model; The idea of learning in context. The model can prompt for some examples and continue with the pattern without updating the weights. This document is important because it explains why prompts have become so powerful. This will help you understand why LLMs can answer questions, summarize text, translate, write code, and follow examples without having to be retrained for each task.
# 3. Scaling law for neurolinguistic models
this Scaling law for neurolinguistic models The paper tried to answer practical questions: What happens when you make your language model bigger, train it with more data, and use more compute? This showed that the performance of the model improves in a predictable manner as parameters, data, and computation increase. This paper discusses the scaling aspects of modern LLM and explains why the field has moved towards larger models and larger training runs. This is important because it provides the system-level logic behind modern LLM training. This helps explain why companies invest heavily in large models, large datasets, and large compute clusters. It also provides a useful foundation for understanding emerging discussions about compute optimization training, data quality, and efficient model scaling.
# 4. Training language models to follow instructions with human feedback
this is, Teach GPT paper. Describes how the base language model can serve as an assistant. A pre-trained model is better at predicting text, but that doesn’t mean it will automatically follow instructions and produce helpful, safe responses. This paper uses the following training process. Supervised fine-tuning and reinforcement learning from human feedback (RLHF). First, a human writes an appropriate example answer. A human then ranks the model outputs. These rankings are used to train a reward model, and the language model is further optimized to produce responses that humans prefer. This paper is important because it explains the difference between a raw language model and an assistant that follows instructions. If you want to understand why the chat model behaves differently than the base model, read on.
# 5. Search extension generation for knowledge-intensive NLP tasks
this Search expansion generation for knowledge-intensive NLP tasks This paper describes search augmentation generation (RAG). The main idea is that language models do not have to rely solely on knowledge stored in parameters. You can retrieve relevant documentation from external sources and use them to generate better answers. This paper combines a pre-trained generative model with a dense retriever and document index. This allows the model to access external knowledge while generating a response. This is especially useful for answering questions, fact-based tasks, and situations where information changes over time. This document is important because many real-world LLM applications use some form of acquisition. Chatbots, enterprise assistants, search systems, customer support agents, and documentation tools often use RAGs to aggregate responses to specific sources.
# summary
Taken together, these five papers provide an overview of how modern LLMs work.
Transformer Architecture → Pre-Training → Scaling → Instruction Tuning → Search Augment Generation
Don’t worry if you don’t understand all the equations and technical details the first time you read it. The aim is simply to understand the main idea behind each paper and why it is important. Once you do, most LLM concepts will start to make more sense.
kanwar mereen I’m a machine learning engineer and technical writer with a deep passion for the intersection of data science, AI, and healthcare. She co-authored the e-book “Maximize Productivity with ChatGPT.” She champions diversity and academic excellence as a 2022 Google Generation Scholar for APAC. She has also been recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and a Harvard WeCode Scholar. Kanwal is a passionate advocate for change and founded FEMCodes to empower women in STEM fields.
