Engineers' guide to LLMs covers Transformers, RAG, and evaluation pitfalls

This paper provides a plain-language overview of large language models (LLMs) for engineers, detailing their underlying architecture and training processes. It explains key concepts such as the Transformer architecture, pre-training, fine-tuning, and reinforcement learning from human feedback. The overview also covers advanced techniques like retrieval-augmented generation (RAG), chain-of-thought prompting, and the use of agents with tools, alongside efficiency methods like FlashAttention and QLoRA. Finally, it offers guidance on evaluating LLM claims, highlighting potential issues like data contamination, LLM-as-a-judge evaluations, long context handling, and hallucination. AI

IMPACT Provides engineers with a foundational understanding of LLM technology, enabling better utilization and evaluation of AI tools.

RANK_REASON Academic paper providing an overview of LLM concepts and techniques. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• ChatGPT
• Flashattention
• Large Language Models
• QLoRA
• Quantized Low-Rank Adaptation
• reinforcement learning from human feedback
• retrieval-augmented generation
• Transformer

• paper
• other

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