5 Fun Papers That Explain LLMs Clearly
This article discusses five key papers that clarify the workings of large language models (LLMs). Each paper addresses a fundamental aspect of LLMs, from the Transformer architecture to instruction-following capabilities. By exploring these papers, readers can gain a better understanding of how LLMs function and their practical applications.
- ▪The 'Attention Is All You Need' paper introduced the Transformer architecture, which is foundational for modern LLMs.
- ▪The GPT-3 paper explains in-context learning, allowing LLMs to perform multiple tasks based on prompts without retraining.
- ▪The 'Scaling Laws for Neural Language Models' paper discusses how increasing model size and data improves performance predictably.
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# Introduction Large language models (LLMs) can feel complicated at first. There are transformers, attention layers, scaling laws, pretraining, instruction tuning, human feedback, retrieval, and many other ideas around them. But the best way to understand large language models is not to start with a huge textbook. A better way is to read a few important papers that each explain one major part of the system. This article is part of a fun series where we learn by exploring core ideas, practical projects, and the research papers behind modern technology. In this article, we will go through five papers that explain how LLMs work. So, let's get started. # 1.
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