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Eigenpruning: an Interpretability-Inspired PEFT Method

Eigenpruning: an Interpretability-Inspired PEFT Method

4 April 2024
T. Browne
Álvaro Soto
A. Aizawa
ArXivPDFHTML

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
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
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
How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model
Michael Hanna
Ollie Liu
Alexandre Variengien
LRM
189
119
0
30 Apr 2023
Interpretability in the Wild: a Circuit for Indirect Object
  Identification in GPT-2 small
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
494
0
01 Nov 2022
Fast Model Editing at Scale
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
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
1