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Evaluating Open-Source Sparse Autoencoders on Disentangling Factual
  Knowledge in GPT-2 Small

Evaluating Open-Source Sparse Autoencoders on Disentangling Factual Knowledge in GPT-2 Small

5 September 2024
Maheep Chaudhary
Atticus Geiger
ArXivPDFHTML

Papers citing "Evaluating Open-Source Sparse Autoencoders on Disentangling Factual Knowledge in GPT-2 Small"

4 / 4 papers shown
Title
MIB: A Mechanistic Interpretability Benchmark
MIB: A Mechanistic Interpretability Benchmark
Aaron Mueller
Atticus Geiger
Sarah Wiegreffe
Dana Arad
Iván Arcuschin
...
Alessandro Stolfo
Martin Tutek
Amir Zur
David Bau
Yonatan Belinkov
43
1
0
17 Apr 2025
Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words
Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words
Gouki Minegishi
Hiroki Furuta
Yusuke Iwasawa
Y. Matsuo
49
1
0
09 Jan 2025
Analyzing (In)Abilities of SAEs via Formal Languages
Analyzing (In)Abilities of SAEs via Formal Languages
Abhinav Menon
Manish Shrivastava
David M. Krueger
Ekdeep Singh Lubana
42
7
0
15 Oct 2024
Residual Stream Analysis with Multi-Layer SAEs
Residual Stream Analysis with Multi-Layer SAEs
Tim Lawson
Lucy Farnik
Conor Houghton
Laurence Aitchison
26
3
0
06 Sep 2024
1