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Embeddings: What they are and why they matter (simonwillison.net)

668 points by simonw · 1035 days ago · 131 comments on HN

Article summary

The article discusses embeddings, a technique used in natural language processing and machine learning to represent content as arrays of floating point numbers. This allows for the calculation of semantic similarities between pieces of content, enabling applications such as related content search and semantic search. The article explains how embeddings work and provides examples of their use, including a project that used embeddings to build a related content feature for a blog. The article also touches on the use of openly licensed models and the potential risks of relying on proprietary models.

Main themes

  • embeddings
  • natural language processing
  • machine learning
  • semantic search
  • related content
  • document search
  • chunking strategies
  • history of embeddings
  • vector databases
  • language models
  • hallucinations

What commenters say

  • Embeddings are a powerful tool for navigating between documents based on similarities, but may not always be ideal for certain use cases.
  • The choice of chunking strategy for document search is dependent on the specific application and can be frustratingly hard to determine.
  • Some commentators argue that embeddings are not always safe and can struggle with logical opposites, while others see them as a cornerstone of machine learning.
  • The history of embeddings is complex and involves the combination of several key components, including vectorial representations, distributed representations, and neural approaches.
  • There are different opinions on how to best generate embeddings from input queries, with some suggesting the use of paragraph-sized chunks and others proposing more complex strategies.
  • The use of embeddings for document search is still a cutting-edge area and can be unreliable and inaccurate without significant work.
  • Some commentators see embeddings as a way to avoid hallucinations in language models, while others argue that they are not a panacea and can have their own limitations.