Fazl Barez

According to our database, Fazl Barez authored at least 49 papers between 2021 and 2025.

Collaborative distances:

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

2025

Rethinking Safety in LLM Fine-tuning: An Optimization Perspective.

Minseon Kim, Jin Myung Kwak, Lama Alssum, Bernard Ghanem, Philip Torr, David Krueger, Fazl Barez, Adel Bibi

CoRR, August, 2025

Establishing Best Practices for Building Rigorous Agentic Benchmarks.

Yuxuan Zhu, Tengjun Jin, Yada Pruksachatkun, Andy Zhang, Shu Liu, Sasha Cui, Sayash Kapoor, Shayne Longpre, Kevin Meng, Rebecca Weiss, Fazl Barez, Rahul Gupta, Jwala Dhamala, Jacob Merizian, Mario Giulianelli, Harry Coppock, Cozmin Ududec, Jasjeet S. Sekhon, Jacob Steinhardt, Antony Kellerman, Sarah Schwettmann, Matei Zaharia, Ion Stoica, Percy Liang, Daniel Kang

CoRR, July, 2025

The Singapore Consensus on Global AI Safety Research Priorities.

Yoshua Bengio, Tegan Maharaj, Luke Ong, Stuart Russell, Dawn Song, Max Tegmark, Lan Xue, Ya-Qin Zhang, Stephen Casper, Wan Sie Lee, Sören Mindermann, Vanessa Wilfred, Vidhisha Balachandran, Fazl Barez, Michael Belinsky, Imane Bello, Malo Bourgon, Mark Brakel, Siméon Campos, Duncan Cass-Beggs, Jiahao Chen, Rumman Chowdhury, Kuan Chua Seah, Jeff Clune, Juntao Dai, Agnès Delaborde, Nouha Dziri, Francisco Eiras, Joshua Engels, Jinyu Fan, Adam Gleave, Noah Goodman, Fynn Heide, Johannes Heidecke, Dan Hendrycks, Cyrus Hodes, Bryan Low Kian Hsiang, Minlie Huang, Sami Jawhar, Wang Jingyu, Adam Tauman Kalai, Meindert Kamphuis, Mohan S. Kankanhalli, Subhash Kantamneni, Mathias Bonde Kirk, Thomas Kwa, Jeffrey Ladish, Kwok-Yan Lam, Wan Lee Sie, Taewhi Lee, Xiaojian Li, Jiajun Liu, Chaochao Lu, Yifan Mai, Richard Mallah, Julian Michael, Nick Moës, Simon Möller, Kihyuk Nam, Kwan Yee Ng, Mark Nitzberg, Besmira Nushi, Seán Ó hÉigeartaigh, Alejandro Ortega, Pierre Peigné, James Petrie, Benjamin Prud'homme, Reihaneh Rabbany, Nayat Sanchez-Pi, Sarah Schwettmann, Buck Shlegeris, Saad Siddiqui, Aradhana Sinha, Martín Soto, Cheston Tan, Dong Ting, William-Chandra Tjhi, Robert Trager, Brian Tse, Anthony Tung K. H., John Willes, Denise Wong, Wei Xu, Rongwu Xu, Yi Zeng, HongJiang Zhang, Djordje Zikelic

CoRR, June, 2025

Beyond Monoliths: Expert Orchestration for More Capable, Democratic, and Safe Large Language Models.

Philip Quirke, Narmeen Oozeer, Chaithanya Bandi, Amir Abdullah, Jason Hoelscher-Obermaier, Jeff M. Phillips, Joshua Greaves, Clement Neo, Fazl Barez, Shriyash Upadhyay

CoRR, June, 2025

Beyond Linear Steering: Unified Multi-Attribute Control for Language Models.

Narmeen Oozeer, Luke Marks, Fazl Barez, Amirali Abdullah

CoRR, May, 2025

Precise In-Parameter Concept Erasure in Large Language Models.

Yoav Gur-Arieh, Clara Suslik, Yihuai Hong, Fazl Barez, Mor Geva

CoRR, May, 2025

SafetyNet: Detecting Harmful Outputs in LLMs by Modeling and Monitoring Deceptive Behaviors.

Maheep Chaudhary, Fazl Barez

CoRR, May, 2025

Scaling sparse feature circuit finding for in-context learning.

Dmitrii Kharlapenko, Stepan Shabalin, Fazl Barez, Arthur Conmy, Neel Nanda

CoRR, April, 2025

In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate?

Ben Bucknall, Saad Siddiqui, Lara Thurnherr, Conor McGurk, Ben Harack, Anka Reuel, Patricia Paskov, Casey Mahoney, Sören Mindermann, Scott Singer, Vinay Hiremath, Charbel-Raphaël Ségerie, Oscar Delaney, Alessandro Abate, Fazl Barez, Michael K. Cohen, Philip Torr, Ferenc Huszár, Anisoara Calinescu, Gabriel Davis Jones, Yoshua Bengio, Robert Trager

CoRR, April, 2025

AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons.

Shaona Ghosh, Heather Frase, Adina Williams, Sarah Luger, Paul Röttger, Fazl Barez, Sean McGregor, Kenneth Fricklas, Mala Kumar, Quentin Feuillade-Montixi, Kurt Bollacker, Felix Friedrich, Ryan Tsang, Bertie Vidgen, Alicia Parrish, Chris Knotz, Eleonora Presani, Jonathan Bennion, Marisa Ferrara Boston, Mike Kuniavsky, Wiebke Hutiri, James Ezick, Malek Ben Salem, Rajat Sahay, Sujata S. Goswami, Usman Gohar, Ben Huang, Supheakmungkol Sarin, Elie Alhajjar, Canyu Chen, Roman Eng, Kashyap Ramanandula Manjusha, Virendra Mehta, Eileen Long, Murali Emani, Natan Vidra, Benjamin Rukundo, Abolfazl Shahbazi, Kongtao Chen, Rajat Ghosh, Vithursan Thangarasa, Pierre Peigné, Abhinav Singh, Max Bartolo, Satyapriya Krishna, Mubashara Akhtar, Rafael Gold, Cody Coleman, Luis Oala, Vassil Tashev, Joseph Marvin Imperial, Amy Russ, Sasidhar Kunapuli, Nicolas Miailhe, Julien Delaunay, Bhaktipriya Radharapu, Rajat Shinde, Tuesday, Debojyoti Dutta, Declan Grabb, Ananya Gangavarapu, Saurav Sahay, Agasthya Gangavarapu, Patrick Schramowski, Stephen Singam, Tom David, Xudong Han, Priyanka Mary Mammen, Tarunima Prabhakar, Venelin Kovatchev, Ahmed Ahmed, Kelvin N. Manyeki, Sandeep Madireddy, Foutse Khomh, Fedor Zhdanov, Joachim Baumann, Nina Vasan, Xianjun Yang, Carlos Mougn, Jibin Rajan Varghese, Hussain Chinoy, Seshakrishna Jitendar, Manil Maskey, Claire V. Hardgrove, Tianhao Li, Aakash Gupta, Emil Joswin, Yifan Mai, Shachi H. Kumar, Cigdem Patlak, Kevin Lu, Vincent Alessi, Sree Bhargavi Balija, Chenhe Gu, Robert Sullivan, James Gealy, Matt Lavrisa, James Goel, Peter Mattson, Percy Liang, Joaquin Vanschoren

CoRR, March, 2025

Same Question, Different Words: A Latent Adversarial Framework for Prompt Robustness.

Tingchen Fu, Fazl Barez

CoRR, March, 2025

Do Sparse Autoencoders Generalize? A Case Study of Answerability.

Lovis Heindrich, Philip Torr, Fazl Barez, Veronika Thost

CoRR, February, 2025

Trust Me, I'm Wrong: High-Certainty Hallucinations in LLMs.

Adi Simhi, Itay Itzhak, Fazl Barez, Gabriel Stanovsky, Yonatan Belinkov

CoRR, February, 2025

Rethinking AI Cultural Evaluation.

Michal Bravansky, Filip Trhlík, Fazl Barez

CoRR, January, 2025

Open Problems in Machine Unlearning for AI Safety.

Fazl Barez, Tingchen Fu, Ameya Prabhu, Stephen Casper, Amartya Sanyal, Adel Bibi, Aidan O'Gara, Robert Kirk, Ben Bucknall, Timothy Fist, Luke Ong, Philip Torr, Kwok-Yan Lam, Robert Trager, David Krueger, Sören Mindermann, José Hernández-Orallo, Mor Geva, Yarin Gal

CoRR, January, 2025

Towards Interpreting Visual Information Processing in Vision-Language Models.

Clement Neo, Luke Ong, Philip Torr, Mor Geva, David Krueger, Fazl Barez

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate?

Ben Bucknall, Saad Siddiqui, Lara Thurnherr, Conor McGurk, Ben Harack, Anka Reuel, Patricia Paskov, Casey Mahoney, Sören Mindermann, Scott Singer, Vinay Hiremath, Charbel-Raphaël Ségerie, Oscar Delaney, Alessandro Abate, Fazl Barez, Michael K. Cohen, Philip Torr, Ferenc Huszár, Anisoara Calinescu, Gabriel Davis Jones, Robert Trager

Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 2025

2024

Best-of-N Jailbreaking.

John Hughes, Sara Price, Aengus Lynch, Rylan Schaeffer, Fazl Barez, Sanmi Koyejo, Henry Sleight, Erik Jones, Ethan Perez, Mrinank Sharma

CoRR, 2024

Jailbreak Defense in a Narrow Domain: Limitations of Existing Methods and a New Transcript-Classifier Approach.

Tony T. Wang, John Hughes, Henry Sleight, Rylan Schaeffer, Rajashree Agrawal, Fazl Barez, Mrinank Sharma, Jesse Mu, Nir Shavit, Ethan Perez

CoRR, 2024

Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders.

Luke Marks, Alasdair Paren, David Krueger, Fazl Barez

CoRR, 2024

PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning.

Tingchen Fu, Mrinank Sharma, Philip Torr, Shay B. Cohen, David Krueger, Fazl Barez

CoRR, 2024

Sparse Autoencoders Reveal Universal Feature Spaces Across Large Language Models.

Michael Lan, Philip Torr, Austin Meek, Ashkan Khakzar, David Krueger, Fazl Barez

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

Risks and Opportunities of Open-Source Generative AI.

Francisco Eiras, Aleksandar Petrov, Bertie Vidgen, Christian Schröder de Witt, Fabio Pizzati, Katherine Elkins, Supratik Mukhopadhyay, Adel Bibi, Aaron Purewal, Botos Csaba, Fabro Steibel, Fazel Keshtkar, Fazl Barez, Genevieve Smith, Gianluca Guadagni, Jon Chun, Jordi Cabot, Joseph Marvin Imperial, Juan Arturo Nolazco, Lori Landay, Matthew Thomas Jackson, Philip H. S. Torr, Trevor Darrell, Yong Suk Lee, Jakob N. Foerster

CoRR, 2024

Visualizing Neural Network Imagination.

Nevan Wichers, Victor Tao, Riccardo Volpato, Fazl Barez

CoRR, 2024

Near to Mid-term Risks and Opportunities of Open Source Generative AI.

Francisco Eiras, Aleksandar Petrov, Bertie Vidgen, Christian Schröder de Witt, Fabio Pizzati, Katherine Elkins, Supratik Mukhopadhyay, Adel Bibi, Botos Csaba, Fabro Steibel, Fazl Barez, Genevieve Smith, Gianluca Guadagni, Jon Chun, Jordi Cabot, Joseph Marvin Imperial, Juan A. Nolazco-Flores, Lori Landay, Matthew Thomas Jackson, Paul Röttger, Philip H. S. Torr, Trevor Darrell, Yong Suk Lee, Jakob N. Foerster

CoRR, 2024

Increasing Trust in Language Models through the Reuse of Verified Circuits.

Philip Quirke, Clement Neo, Fazl Barez

CoRR, 2024

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training.

Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M. Ziegler, Tim Maxwell, Newton Cheng, Adam S. Jermyn, Amanda Askell, Ansh Radhakrishnan, Cem Anil, David Duvenaud, Deep Ganguli, Fazl Barez, Jack Clark, Kamal Ndousse, Kshitij Sachan, Michael Sellitto, Mrinank Sharma, Nova DasSarma, Roger B. Grosse, Shauna Kravec, Yuntao Bai, Zachary Witten, Marina Favaro, Jan Brauner, Holden Karnofsky, Paul F. Christiano, Samuel R. Bowman, Logan Graham, Jared Kaplan, Sören Mindermann, Ryan Greenblatt, Buck Shlegeris, Nicholas Schiefer, Ethan Perez

CoRR, 2024

Interpreting Learned Feedback Patterns in Large Language Models.

Luke Marks, Amir Abdullah, Clement Neo, Rauno Arike, David Krueger, Philip Torr, Fazl Barez

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Value-Evolutionary-Based Reinforcement Learning.

Pengyi Li, Jianye Hao, Hongyao Tang, Yan Zheng, Fazl Barez

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI.

Francisco Eiras, Aleksandar Petrov, Bertie Vidgen, Christian Schröder de Witt, Fabio Pizzati, Katherine Elkins, Supratik Mukhopadhyay, Adel Bibi, Botos Csaba, Fabro Steibel, Fazl Barez, Genevieve Smith, Gianluca Guadagni, Jon Chun, Jordi Cabot, Joseph Marvin Imperial, Juan A. Nolazco-Flores, Lori Landay, Matthew Thomas Jackson, Paul Röttger, Philip H. S. Torr, Trevor Darrell, Yong Suk Lee, Jakob N. Foerster

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Understanding Addition in Transformers.

Philip Quirke, Fazl Barez

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Interpreting Context Look-ups in Transformers: Investigating Attention-MLP Interactions.

Clement Neo, Shay B. Cohen, Fazl Barez

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Towards Interpretable Sequence Continuation: Analyzing Shared Circuits in Large Language Models.

Michael Lan, Philip Torr, Fazl Barez

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Large Language Models Relearn Removed Concepts.

Michelle Lo, Fazl Barez, Shay B. Cohen

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

Measuring Value Alignment.

Fazl Barez, Philip H. S. Torr

CoRR, 2023

Locating Cross-Task Sequence Continuation Circuits in Transformers.

Michael Lan, Fazl Barez

CoRR, 2023

Interpreting Reward Models in RLHF-Tuned Language Models Using Sparse Autoencoders.

Luke Marks, Amir Abdullah, Luna Mendez, Rauno Arike, Philip H. S. Torr, Fazl Barez

CoRR, 2023

AI Systems of Concern.

Kayla Matteucci, Shahar Avin, Fazl Barez, Seán Ó hÉigeartaigh

CoRR, 2023

DeepDecipher: Accessing and Investigating Neuron Activation in Large Language Models.

Albert Garde, Esben Kran, Fazl Barez

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

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

System III: Learning with Domain Knowledge for Safety Constraints.

Fazl Barez, Hosien Hasanbieg, Alesandro Abbate

CoRR, 2023

Fairness in AI and Its Long-Term Implications on Society.

Ondrej Bohdal, Timothy M. Hospedales, Philip H. S. Torr, Fazl Barez

CoRR, 2023

Exploring the Advantages of Transformers for High-Frequency Trading.

Fazl Barez, Paul Bilokon, Arthur Gervais, Nikita Lisitsyn

CoRR, 2023

Benchmarking Specialized Databases for High-frequency Data.

Fazl Barez, Paul Bilokon, Ruijie Xiong

CoRR, 2023

Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark.

Jason Hoelscher-Obermaier, Julia Persson, Esben Kran, Ioannis Konstas, Fazl Barez

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

The Larger they are, the Harder they Fail: Language Models do not Recognize Identifier Swaps in Python.

Antonio Valerio Miceli Barone, Fazl Barez, Shay B. Cohen, Ioannis Konstas

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2021

ED2: An Environment Dynamics Decomposition Framework for World Model Construction.

Cong Wang, Tianpei Yang, Jianye Hao, Yan Zheng, Hongyao Tang, Fazl Barez, Jinyi Liu, Jiajie Peng, Haiyin Piao, Zhixiao Sun

CoRR, 2021