Approximating How Single Head Attention Learns - Crossminds
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Approximating How Single Head Attention Learns
Key knowledge areas and research papers related to the new publication “Approximating How Single Head Attention Learns” authored by Charlie Snell, Ruiqi Zhong, Dan Klein, and Jacob Steinhardt from UC Berkeley.
Top related arXiv papers
Key knowledge areas
Other recommended papers

Shikhar Vashishth, Attention Interpretability Across NLP Tasks. ArXiv 2019

Gino Brunner, On Identifiability in Transformers. ICLR 2020