The hottest Transformers Substack posts right now

And their main takeaways
Category
Top Technology Topics

LLMs – Part 3: Context Matters — Self Attention

Vasu’s Newsletter • 78 implied HN points • 25 Jan 26
🕹 Technology Transformers
  1. Each token creates query, key, and value vectors so it can ask what it needs, match that against other tokens, and gather useful information.
  2. Tokens compare their query to every key to get raw scores, convert those scores to attention weights with softmax, and use the weights to take a weighted sum of value vectors to produce a new contextual vector.
  3. Self-attention makes token meanings contextual (helping with pronouns, disambiguation, and long-range links), and models use multiple attention heads plus feed-forward layers to capture different relation patterns and refine each token's representation.

Make softmax great again

Gonzo ML • 126 implied HN points • 06 Nov 24
🕹 Technology Transformers
  1. Softmax is widely used in machine learning, especially in transformers, to turn numbers into probabilities. However, it struggles when dealing with new kinds of data that the model hasn't seen before.
  2. The sharpness of softmax can fade when there's a lot of input data. This means it sometimes can't make clear predictions about which option is best in bigger datasets.
  3. To improve softmax, researchers suggest using 'adaptive temperature.' This idea helps make the predictions sharper based on the data being processed, leading to better performance in some tasks.

Electricity

Barn Lab • 0 implied HN points • 21 May 23
🔬 Science Transformers
  1. Generators produce electricity when a wire is present within a magnetic field, requiring movement or change for electrical output.
  2. Transformers modify AC by increasing or decreasing voltage and amperage without moving parts, crucial for transmitting energy efficiently.
  3. Capacitors store electrical charge and can act as filters, allowing the passage of AC while blocking DC, useful for various applications like frequency filtering.

LLMs for Dummies

Digital Native • 0 implied HN points • 12 Oct 23
🕹 Technology Transformers
  1. Large language models (LLMs) like GPT-3 have rapidly improved in recent years, showing exponential growth in size and capability.
  2. LLMs work by translating words into numbers using word vectors stored in multidimensional planes, helping to capture relationships between words.
  3. There are various frameworks for LLM applications, such as solving impossible problems, simplifying complex tasks, focusing on vertical AI products, and creating AI copilot tools for faster and more efficient human work.

Why Transformers Are Good

From AI to ZI • 0 implied HN points • 19 Jan 24
🕹 Technology Transformers
  1. Transformers have a parameter-efficient way of passing information between tokens.
  2. Sharing parameters across all positions saves computational resources.
  3. Training with transformers allows for parallelization and speeding up computations.
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Introduction to Neurons, Backpropagation and Transformers

Rob Leclerc • 0 implied HN points • 10 Jul 24
🕹 Technology Transformers
  1. Neurons process information through reception, transmission, integration, propagation, and communication, illustrating a fundamental understanding of neural dynamics.
  2. Backpropagation is a key algorithm in training neural networks, involving forward pass, error calculation, backward pass, and weight update to optimize network performance.
  3. Artificial neural networks have evolved from single-layer perceptrons to multi-layer perceptrons, showcasing the importance of hierarchical learning and specialized architectures for different tasks.