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Scaling Monosemanticity: Anthropics One Step Towards Interpretable & Manipulable Llms

From prompt engineering to activation engineering for more controllable and safer LLMs
Towards Data Science 6:38 pm on May 28, 2024

Scaling Monosemanticity advances towards interpretability and manipulation of LLMs by enhancing monosemanticity over polysemanticity for improved control and safety. Jack Chih-Hsu Lin explores this in a Towards Data Science article, emphasizing the shift from prompt engineering to activation engineering.

Advancement of LLM Interpretability: Scaling Monosemanticity improves clarity and controllability.
Shift in Engineering Focus: Transition from prompt to activation engineering for better oversight.
Neural Networks Comparison: Differentiates between monosemanticity and polysemantic neural network design.
Contributor Details: Jack Chih-Hsu Lin, GenAI at C3.ai for Data Science innovations.
Content Platform: Published on Towards Data Science blog with an emphasis on AI advancements.

https://towardsdatascience.com/scaling-monosemanticity-anthropics-one-step-towards-interpretable-manipulable-llms-4b9403c4341e

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Scaling Monosemanticity: Anthropics One Step Towards Interpretable & Manipulable Llms

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