Learning on Transformers is Provable Low-Rank and Sparse: A One-layer
Analysis

Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis

24 June 2024

Meng Wang

Sijia Liu

Pin-Yu Chen

Papers citing "Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis"

7 / 7 papers shown

Title
When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers Hongkang Li Yihua Zhang Shuai Zhang M. Wang Sijia Liu Pin-Yu Chen MoMe 57 2 0 15 Apr 2025
From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency Kaiyue Wen Huaqing Zhang Hongzhou Lin Jingzhao Zhang MoE LRM 58 2 0 07 Oct 2024
Sparks of Artificial General Intelligence: Early experiments with GPT-4 Sébastien Bubeck Varun Chandrasekaran Ronen Eldan J. Gehrke Eric Horvitz ... Scott M. Lundberg Harsha Nori Hamid Palangi Marco Tulio Ribeiro Yi Zhang ELM AI4MH AI4CE ALM 206 2,232 0 22 Mar 2023
How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding Yuchen Li Yuan-Fang Li Andrej Risteski 107 61 0 07 Mar 2023
SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics Emmanuel Abbe Enric Boix-Adserà Theodor Misiakiewicz FedML MLT 76 72 0 21 Feb 2023
Learning Single-Index Models with Shallow Neural Networks A. Bietti Joan Bruna Clayton Sanford M. Song 160 65 0 27 Oct 2022
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 301 11,730 0 04 Mar 2022