[2402.09221] Spectral Filters, Dark Signals, and Attention Sinks

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. arXiv Operational Status Get status notifications via email or slack

[2402.09221] Spectral Filters, Dark Signals, and Attention Sinks Skip to main content arXiv is now an independent nonprofit! Learn more × Search arXiv Press Enter to search · Advanced search --> Computer Science > Artificial Intelligence arXiv:2402.09221 (cs) [Submitted on 14 Feb 2024] Title: Spectral Filters, Dark Signals, and Attention Sinks Authors: Nicola Cancedda View a PDF of the paper titled Spectral Filters, Dark Signals, and Attention Sinks, by Nicola Cancedda View PDF HTML (experimental) Abstract: Projecting intermediate representations onto the vocabulary is an increasi

related reading

• A Mathematical Framework for Transformer Circuitstransformer-circuits.pub
• Circuit Tracing: Revealing Computational Graphs in Language Modelstransformer-circuits.pub
• Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnettransformer-circuits.pub
• Transformer Circuits Threadtransformer-circuits.pub
• Transformer Explainer: LLM Transformer Model Visually Explainedpoloclub.github.io
• Transformers from Scratche2eml.school
• On the Tradeoffs of SSMs and Transformers | Goomba Labgoombalab.github.io
• Getting Caught Up to Modern LLM Research | Samarth Goeldev.samarthgoel.com
• Sparse Attention Post-Training for Mechanistic Interpretabilityarxiv.org
• 2502.11089arxiv.org
• LLM Resourcesforrestbicker.com
• [2012.09852] SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruningarxiv.org