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RAVEL: Evaluating Interpretability Methods on Disentangling Language
  Model Representations

RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations

27 February 2024
Jing-ling Huang
Zhengxuan Wu
Christopher Potts
Mor Geva
Atticus Geiger
ArXivPDFHTML

Papers citing "RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations"

10 / 10 papers shown
Title
HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks
HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks
Jiuding Sun
Jing Huang
Sidharth Baskaran
Karel DÓosterlinck
Christopher Potts
Michael Sklar
Atticus Geiger
AI4CE
55
0
0
13 Mar 2025
A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models
A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models
Daking Rai
Yilun Zhou
Shi Feng
Abulhair Saparov
Ziyu Yao
47
18
0
02 Jul 2024
The Geometry of Truth: Emergent Linear Structure in Large Language Model
  Representations of True/False Datasets
The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets
Samuel Marks
Max Tegmark
HILM
83
164
0
10 Oct 2023
Finding Neurons in a Haystack: Case Studies with Sparse Probing
Finding Neurons in a Haystack: Case Studies with Sparse Probing
Wes Gurnee
Neel Nanda
Matthew Pauly
Katherine Harvey
Dmitrii Troitskii
Dimitris Bertsimas
MILM
150
170
0
02 May 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
170
116
0
30 Apr 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
Disentangled Explanations of Neural Network Predictions by Finding
  Relevant Subspaces
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
Pattarawat Chormai
J. Herrmann
Klaus-Robert Muller
G. Montavon
FAtt
32
13
0
30 Dec 2022
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
What you can cram into a single vector: Probing sentence embeddings for
  linguistic properties
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Alexis Conneau
Germán Kruszewski
Guillaume Lample
Loïc Barrault
Marco Baroni
196
876
0
03 May 2018
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