New Tiled SVD Method Extracts Network Mechanisms Directly From Weights

Researchers have developed a new method called column-tiled SVD to extract usable weight mechanisms directly from linear sites within neural networks. This approach identifies concepts within the network's weights themselves, rather than relying on external proxy dictionaries. The method was evaluated on Gemma-2-2B using WikiText-2, achieving a perfect score of 182/182 across all tested linear maps, with specific maps demonstrating full A/B/C scoring for residual writes. AI

IMPACT This method could improve the understanding and interpretability of large language models by directly analyzing their internal weight mechanisms.

RANK_REASON The item is a research paper published on arXiv detailing a new method for mechanistic interpretability. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• CORE Recommender
• DagsHub
• Gemma 2-2B
• Gotit.pub
• Hugging Face
• Influence Flower
• RMSNorm
• ScienceCast
• singular value decomposition
• WikiText-2

• paper
• model release

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