Understanding Embeddings in Language Models

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Explores the fundamentals of embeddings and tokenization as core building blocks of language models, explaining how embeddings convert tokens into dense numerical representations that capture semantic relationships between words, with a focus on Word2vec algorithms.

The whole book looks like a ton of fun.

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Connections

Supports

Compression is prediction Embeddings fundamentally encode compression and prediction — the piece on 'compression is prediction' directly relates to how dense numerical representations capture semantic meaning through learned statistical patterns
LLMs can't jump 'LLMs can't jump' likely explores limitations of language models; understanding how embeddings encode semantic space helps explain why certain reasoning or spatial leaps fail in the embedding geometry
Ways to think about token pricing Understanding tokenization and embeddings provides foundational context for token pricing — tokens are the unit of measurement in LLM economics, and understanding what they represent clarifies cost structures
Understanding is the new bottleneck 'Understanding is the new bottleneck' argues comprehension matters more than generation — embeddings as semantic representations are precisely the mechanism by which models encode (or fail to encode) genuine understanding

Related to

Neuronpedia Jacobian Lens: Interactive J-Space Explorer for Qwen3.6-27B Neuronpedia's Jacobian Lens explores the internal representational space of transformer models — directly extending embedding fundamentals into mechanistic interpretability of how representations transform through layers
DeepSeek V4 Pro 0813 API Pricing & Benchmarks Benchmarking and comparing LLM capabilities implicitly depends on what embeddings encode — model quality comparisons often reduce to how well embedding spaces capture semantic relationships

Type:	link
First published:	Thursday, 20 August 2026 at 23:33 UTC
Location:	https://www.maayanroth.com/blog/posts/hundred-page-lm-book-chapter-2.html
URL:	https://mattwood.fyi/i/fccee7ea