Writing Facts Directly Into Transformer MLPs Without Training
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- x.com
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This amazing team shows how to build knowledge directly into Transformer blocks **without gradient descent**! https://t.co/wGY4frt5A9
Context
Research shared by Stanford AI Lab describes a way to construct the fact-storing part of a , its MLP layers, directly from known associations. In the researchers' explanation, facts pair a key with a value, such as a country with its capital. A mathematical recipe sets the component's weights without repeatedly adjusting them through training.
When queried, the constructed component compares the input with stored keys and combines their associated values. The work provides a controlled model of factual memory inside Transformer blocks. Understanding and editing the corresponding components in existing pretrained language models remains an open research question.
- transformer — The neural network architecture behind modern AI models, built on attention — letting every word directly consider every other word in parallel.
- LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
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