5 Fun Papers That Explain LLMs Clearly

1️⃣ Attention Is All You Need

📝 Description: Introduced the Transformer, the architecture behind every modern LLM. Replaced older recurrent/convolutional models for sequences.
🔑 Key Ideas: Self-attention • Multi-head attention • Positional encoding • Transformer block
🔗 Paper: https://arxiv.org/abs/1706.03762
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2️⃣ Language Models Are Few-Shot Learners
📝 Description: The GPT-3 paper. One 175B model handles many tasks just by reading prompts — no retraining.
🔑 Key Ideas: In-context learning • Few-shot prompting • Autoregressive next-token prediction
🔗 Paper: https://arxiv.org/abs/2005.14165
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3️⃣ Scaling Laws for Neural Language Models
📝 Description: Showed model performance improves predictably as parameters, data & compute grow. The logic behind going big.
🔑 Key Ideas: Scaling laws • Compute-optimal training • Data vs. model size tradeoffs
🔗 Paper: https://arxiv.org/abs/2001.08361
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4️⃣ Training LMs to Follow Instructions with Human Feedback
📝 Description: The InstructGPT paper. Turns a raw text predictor into a helpful, instruction-following assistant.
🔑 Key Ideas: RLHF • Supervised fine-tuning • Reward model • Human preference ranking
🔗 Paper: https://arxiv.org/abs/2203.02155
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5️⃣ Retrieval-Augmented Generation (RAG)
📝 Description: LLMs fetch external documents instead of relying only on stored memory — great for facts that change over time.
🔑 Key Ideas: Dense retrieval • Document index • Grounded generation • Knowledge-intensive QA
🔗 Paper: https://arxiv.org/abs/2005.11401

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