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LLMs predict words from patterns, not memory; faster attention could speed up chats

Large language models do not possess true memory, instead predicting the next word based on patterns learned during training. While model weights remain static, advancements in attention mechanisms could significantly speed up response times, making AI interactions much faster. AI

IMPACT Understanding LLM limitations and potential speed improvements can inform development and user expectations.

RANK_REASON The cluster discusses the fundamental nature of LLMs and potential improvements, but does not announce a new model, research paper, or product.

Read on Mastodon — fosstodon.org →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    Model weights stay fixed; faster attention could make chats blazing fast. # ai # llm # memory

    Model weights stay fixed; faster attention could make chats blazing fast. # ai # llm # memory

  2. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    AI predicts the next word from patterns, not true memory. # ai # llm # memory

    AI predicts the next word from patterns, not true memory. # ai # llm # memory