Concept 1 / 5
Tokenization
Text is split into learned units called tokens. A token is not always a word; spelling, language and formatting change token counts and costs.
学習目標
- Explain tokens, embeddings, attention and the Transformer at a practical level.
- Separate pretraining, post-training and inference.
- Predict why context length, sampling and external memory affect results.
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よくある誤解
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よくある誤解
- A token is not a stable word unit, so word counts and token counts are not interchangeable.
- A larger context window does not guarantee uniform attention; important evidence can still be missed.
- Post-training can shape behavior, but it does not turn generated probabilities into guaranteed truth.
重要語
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重要語
- token
- tokenizer
- embedding
- attention
- Transformer
- pretraining
- SFT
- RLHF
- DPO
- inference
一次研究
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一次研究
Voir les sources +Réduire −Attention Is All You NeedPrimary paper introducing the Transformer architecture.SentencePiecePrimary paper on language-independent subword tokenization from raw text.Training language models to follow instructionsPrimary paper on supervised fine-tuning and reinforcement learning from human feedback.Direct Preference OptimizationPrimary paper presenting direct optimization from preference pairs.Lost in the MiddleStudy of how language models use information placed at different context positions.