Eigenpruning: an Interpretability-Inspired PEFT Method

4 April 2024

Papers citing "Eigenpruning: an Interpretability-Inspired PEFT Method"

6 / 6 papers shown

Title
A Convex-optimization-based Layer-wise Post-training Pruner for Large Language Models Pengxiang Zhao Hanyu Hu Ping Li Yi Zheng Zhefeng Wang Xiaoming Yuan 31 1 0 07 Aug 2024
Attribution Patching Outperforms Automated Circuit Discovery Aaquib Syed Can Rager Arthur Conmy 57 54 0 16 Oct 2023
How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model Michael Hanna Ollie Liu Alexandre Variengien LRM 186 116 0 30 Apr 2023
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small Kevin Wang Alexandre Variengien Arthur Conmy Buck Shlegeris Jacob Steinhardt 210 491 0 01 Nov 2022
Fast Model Editing at Scale E. Mitchell Charles Lin Antoine Bosselut Chelsea Finn Christopher D. Manning KELM 219 341 0 21 Oct 2021
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 294 6,943 0 20 Apr 2018