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pyvene: A Library for Understanding and Improving PyTorch Models via
  Interventions

pyvene: A Library for Understanding and Improving PyTorch Models via Interventions

12 March 2024
Zhengxuan Wu
Atticus Geiger
Aryaman Arora
Jing-ling Huang
Zheng Wang
Noah D. Goodman
Christopher D. Manning
Christopher Potts
    MU
ArXivPDFHTML

Papers citing "pyvene: A Library for Understanding and Improving PyTorch Models via Interventions"

5 / 5 papers shown
Title
Personality Alignment of Large Language Models
Personality Alignment of Large Language Models
Minjun Zhu
Linyi Yang
Yue Zhang
Yue Zhang
ALM
30
5
0
21 Aug 2024
Uncovering Intermediate Variables in Transformers using Circuit Probing
Uncovering Intermediate Variables in Transformers using Circuit Probing
Michael A. Lepori
Thomas Serre
Ellie Pavlick
49
7
0
07 Nov 2023
Dissecting Recall of Factual Associations in Auto-Regressive Language
  Models
Dissecting Recall of Factual Associations in Auto-Regressive Language Models
Mor Geva
Jasmijn Bastings
Katja Filippova
Amir Globerson
KELM
180
152
0
28 Apr 2023
Finding Alignments Between Interpretable Causal Variables and
  Distributed Neural Representations
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations
Atticus Geiger
Zhengxuan Wu
Christopher Potts
Thomas F. Icard
Noah D. Goodman
CML
73
98
0
05 Mar 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
205
486
0
01 Nov 2022
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