"I love you" "too": LLM Attention Explained (kaamvaam.com)

A recent post delves into the mechanics of attention in language models, particularly explaining why a model is more likely to predict "too" after "I love you" rather than an unrelated word like "chair." The explanation hinges on three key concepts: vectors that represent words, the dot product for measuring alignment, and the softmax function to normalize scores. Each word is encoded as a vector, and the model uses learned "filters"—Query, Key, and Value—to evaluate the contextual relationship between words based on their historical usage. This analysis is significant for the AI/ML community as it enhances the understanding of how language models like GPT interpret and generate human-like text. By scoring the Query of the word "you" against the Keys of previously encountered words, the model identifies that "love" closely aligns with the emotional context of "you." The final choice of predicting "too" with a probability of 92% illustrates the model's ability to blend contextual information effectively, further spotlighting the intricate balance of learned language patterns and their representation through vectors, ultimately refining how AI interacts linguistically with users.