Neel Nanda

According to our database, Neel Nanda authored at least 22 papers between 2021 and 2024.

Collaborative distances:

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

2024

AtP*: An efficient and scalable method for localizing LLM behaviour to components.

János Kramár, Tom Lieberum, Rohin Shah, Neel Nanda

CoRR, 2024

Explorations of Self-Repair in Language Models.

Cody Rushing, Neel Nanda

CoRR, 2024

Summing Up the Facts: Additive Mechanisms Behind Factual Recall in LLMs.

Bilal Chughtai, Alan Cooney, Neel Nanda

CoRR, 2024

Universal Neurons in GPT2 Language Models.

Wes Gurnee, Theo Horsley, Zifan Carl Guo, Tara Rezaei Kheirkhah, Qinyi Sun, Will Hathaway, Neel Nanda, Dimitris Bertsimas

CoRR, 2024

2023

Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patching.

Aleksandar Makelov, Georg Lange, Neel Nanda

CoRR, 2023

Training Dynamics of Contextual N-Grams in Language Models.

Lucia Quirke, Lovis Heindrich, Wes Gurnee, Neel Nanda

CoRR, 2023

Linear Representations of Sentiment in Large Language Models.

Curt Tigges, Oskar John Hollinsworth, Atticus Geiger, Neel Nanda

CoRR, 2023

Copy Suppression: Comprehensively Understanding an Attention Head.

Callum McDougall, Arthur Conmy, Cody Rushing, Thomas McGrath, Neel Nanda

CoRR, 2023

Towards Best Practices of Activation Patching in Language Models: Metrics and Methods.

Fred Zhang, Neel Nanda

CoRR, 2023

Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla.

Tom Lieberum, Matthew Rahtz, János Kramár, Neel Nanda, Geoffrey Irving, Rohin Shah, Vladimir Mikulik

CoRR, 2023

Neuron to Graph: Interpreting Language Model Neurons at Scale.

Alex Foote, Neel Nanda, Esben Kran, Ioannis Konstas, Shay B. Cohen, Fazl Barez

CoRR, 2023

Finding Neurons in a Haystack: Case Studies with Sparse Probing.

Wes Gurnee, Neel Nanda, Matthew Pauly, Katherine Harvey, Dmitrii Troitskii, Dimitris Bertsimas

CoRR, 2023

N2G: A Scalable Approach for Quantifying Interpretable Neuron Representations in Large Language Models.

Alex Foote, Neel Nanda, Esben Kran, Ioannis Konstas, Fazl Barez

CoRR, 2023

A Toy Model of Universality: Reverse Engineering how Networks Learn Group Operations.

Bilal Chughtai, Lawrence Chan, Neel Nanda

Proceedings of the International Conference on Machine Learning, 2023

Progress measures for grokking via mechanistic interpretability.

Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, Jacob Steinhardt

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Emergent Linear Representations in World Models of Self-Supervised Sequence Models.

Neel Nanda, Andrew Lee, Martin Wattenberg

Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, 2023

2022

Fully General Online Imitation Learning.

Michael K. Cohen, Marcus Hutter, Neel Nanda

J. Mach. Learn. Res., 2022

In-context Learning and Induction Heads.

Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Scott Johnston, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, Chris Olah

CoRR, 2022

Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom B. Brown, Jack Clark, Sam McCandlish, Chris Olah, Benjamin Mann, Jared Kaplan

CoRR, 2022

Predictability and Surprise in Large Generative Models.

Deep Ganguli, Danny Hernandez, Liane Lovitt, Nova DasSarma, Tom Henighan, Andy Jones, Nicholas Joseph, Jackson Kernion, Benjamin Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Dawn Drain, Nelson Elhage, Sheer El Showk, Stanislav Fort, Zac Hatfield-Dodds, Scott Johnston, Shauna Kravec, Neel Nanda, Kamal Ndousse, Catherine Olsson, Daniela Amodei, Dario Amodei, Tom B. Brown, Jared Kaplan, Sam McCandlish, Chris Olah, Jack Clark

CoRR, 2022

Predictability and Surprise in Large Generative Models.

Deep Ganguli, Danny Hernandez, Liane Lovitt, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Nelson Elhage, Sheer El Showk, Stanislav Fort, Zac Hatfield-Dodds, Tom Henighan, Scott Johnston, Andy Jones, Nicholas Joseph, Jackson Kernian, Shauna Kravec, Ben Mann, Neel Nanda, Kamal Ndousse, Catherine Olsson, Daniela Amodei, Tom B. Brown, Jared Kaplan, Sam McCandlish, Christopher Olah, Dario Amodei, Jack Clark

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

2021

An Empirical Investigation of Learning from Biased Toxicity Labels.

Neel Nanda, Jonathan Uesato, Sven Gowal

CoRR, 2021