Explaining Causal Masking in LLMs with a Small Python Example

A developer's learning project called Building LLM Technologies examines individual language model components through small Python notebooks. The article focuses on causal masking, which stops an autoregressive model from attending to future tokens when predicting the next one. Using a three-token example, the author builds a triangular mask and replaces blocked attention scores with a large negative value. Applying softmax then drives the weights for future positions to effectively zero. The author notes the example is simplified, using predefined scores, no training, and a non-production softmax.