LLM construction: Data, post-training, and evaluation are key, not just architecture

Most explanations of how large language models (LLMs) are built focus on architecture and attention mechanisms, neglecting crucial aspects like data pipelines, post-training techniques, and evaluation methods. A detailed breakdown reveals that the data pipeline involves extensive crawling, extraction, filtering, deduplication, and mixing of vast datasets, with significant human and computational resources dedicated to it. Post-training stages like Supervised Fine-Tuning (SFT) teach format and persona, while Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) refine models based on human preferences, though human labeler biases can lead to verbose outputs. Robust evaluation is critical, as standard metrics like perplexity become less meaningful after certain training stages, and methods like LLM-as-judge offer faster, cheaper alternatives to human evaluation like Chatbot Arena, albeit with their own biases. AI

IMPACT Highlights the critical, often overlooked, components of LLM development beyond architecture, emphasizing data and post-training processes.

RANK_REASON The item is an explanation of LLM construction based on a lecture, not a primary release or significant industry event.

Read on dev.to — LLM tag →

• attention
• Bradley–Terry model
• Building Large Language Models
• ChatGPT
• Chinchilla
• Common Crawl
• CS229
• Direct Preference Optimization
• llama
• LLMs
• Proximal Policy Optimization
• Stanford University
• transformers
• Yann Dubois

• paper
• model release

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