Researchers have raised concerns about the susceptibility of standard transformer attention in incorporating irrelevant context, which can adversely affect generations. They propose System 2 attention as a solution to this issue. Transformers, with their self-attention mechanisms, have revolutionized natural language processing and become the basis of state-of-the-art tasks in different domains.
System 2 Attention (is something you might need too) https://t.co/DgAskNMEUN
System 2 Attention (is something you might need too) Jason Weston, Sainbayar Sukhbaatar : https://t.co/23KuENfPCa #ArtificialIntelligence #DeepLearning #MachineLearning https://t.co/EfAvQPQgiy
Transformers have revolutionized natural language processing with their use of self-attention mechanisms. In this post, we will study the key components of transformers to understand how they have become the basis of the state of the art in different tasks https://t.co/n2axsQf9Ll
[CL] System 2 Attention (is something you might need too) J Weston, S Sukhbaatar [Meta] (2023) https://t.co/u6fLDVosrR - Standard transformer attention is susceptible to incorporating irrelevant context, which can adversely affect generations. - The authors propose System 2… https://t.co/DLRQS6nW5a https://t.co/gHptMYyTAC
System 2 Attention (is something you might need too) paper page: https://t.co/zpRfeZOpNT Soft attention in Transformer-based Large Language Models (LLMs) is susceptible to incorporating irrelevant information from the context into its latent representations, which adversely… https://t.co/HsHySuHlV0 https://t.co/npQGw0wsOj