Samuel Marks

According to our database, Samuel Marks authored at least 9 papers between 2023 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Erasing Conceptual Knowledge from Language Models.

Rohit Gandikota, Sheridan Feucht, Samuel Marks, David Bau

CoRR, 2024

The Quest for the Right Mediator: A History, Survey, and Theoretical Grounding of Causal Interpretability.

Aaron Mueller, Jannik Brinkmann, Millicent L. Li, Samuel Marks, Koyena Pal, Nikhil Prakash, Can Rager, Aruna Sankaranarayanan, Arnab Sen Sharma, Jiuding Sun, Eric Todd, David Bau, Yonatan Belinkov

CoRR, 2024

Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models.

Adam Karvonen, Benjamin Wright, Can Rager, Rico Angell, Jannik Brinkmann, Logan Smith, Claudio Mayrink Verdun, David Bau, Samuel Marks

CoRR, 2024

NNsight and NDIF: Democratizing Access to Foundation Model Internals.

Jaden Fiotto-Kaufman, Alexander R. Loftus, Eric Todd, Jannik Brinkmann, Caden Juang, Koyena Pal, Can Rager, Aaron Mueller, Samuel Marks, Arnab Sen Sharma, Francesca Lucchetti, Michael Ripa, Adam Belfki, Nikhil Prakash, Sumeet Multani, Carla E. Brodley, Arjun Guha, Jonathan Bell, Byron C. Wallace, David Bau

CoRR, 2024

Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data.

Johannes Treutlein, Dami Choi, Jan Betley, Cem Anil, Samuel Marks, Roger Baker Grosse, Owain Evans

CoRR, 2024

Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models.

Carson Denison, Monte MacDiarmid, Fazl Barez, David Duvenaud, Shauna Kravec, Samuel Marks, Nicholas Schiefer, Ryan Soklaski, Alex Tamkin, Jared Kaplan, Buck Shlegeris, Samuel R. Bowman, Ethan Perez, Evan Hubinger

CoRR, 2024

Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models.

Samuel Marks, Can Rager, Eric J. Michaud, Yonatan Belinkov, David Bau, Aaron Mueller

CoRR, 2024

2023

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.

Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Tong Wang, Samuel Marks, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell

Trans. Mach. Learn. Res., 2023

The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets.

Samuel Marks, Max Tegmark

CoRR, 2023