Satyapriya Krishna

According to our database, Satyapriya Krishna authored at least 27 papers between 2019 and 2024.

Collaborative distances:

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

2024

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective.

Satyapriya Krishna, Tessa Han, Alex Gu, Steven Wu, Shahin Jabbari, Himabindu Lakkaraju

Trans. Mach. Learn. Res., 2024

Insights from the Inverse: Reconstructing LLM Training Goals Through Inverse RL.

Jared Joselowitz, Arjun Jagota, Satyapriya Krishna, Sonali Parbhoo

CoRR, 2024

Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation.

Satyapriya Krishna, Kalpesh Krishna, Anhad Mohananey, Steven Schwarcz, Adam Stambler, Shyam Upadhyay, Manaal Faruqui

CoRR, 2024

Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs).

Apurv Verma, Satyapriya Krishna, Sebastian Gehrmann, Madhavan Seshadri, Anu Pradhan, Tom Ault, Leslie Barrett, David Rabinowitz, John A. Doucette, NhatHai Phan

CoRR, 2024

More RLHF, More Trust? On The Impact of Human Preference Alignment On Language Model Trustworthiness.

Aaron J. Li, Satyapriya Krishna, Himabindu Lakkaraju

CoRR, 2024

Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence.

Bo Peng, Daniel Goldstein, Quentin Anthony, Alon Albalak, Eric Alcaide, Stella Biderman, Eugene Cheah, Xingjian Du, Teddy Ferdinan, Haowen Hou, Przemyslaw Kazienko, Kranthi Kiran GV, Jan Kocon, Bartlomiej Koptyra, Satyapriya Krishna, Ronald McClelland Jr., Niklas Muennighoff, Fares Obeid, Atsushi Saito, Guangyu Song, Haoqin Tu, Stanislaw Wozniak, Ruichong Zhang, Bingchen Zhao, Qihang Zhao, Peng Zhou, Jian Zhu, Rui-Jie Zhu

CoRR, 2024

Understanding the Effects of Iterative Prompting on Truthfulness.

Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju

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

Black-Box Access is Insufficient for Rigorous AI Audits.

Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas A. Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, Lee Sharkey, Satyapriya Krishna, Marvin Von Hagen, Silas Alberti, Alan Chan, Qinyi Sun, Michael Gerovitch, David Bau, Max Tegmark, David Krueger, Dylan Hadfield-Menell

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

2023

Explaining machine learning models with interactive natural language conversations using TalkToModel.

Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh

Nat. Mac. Intell., August, 2023

On the Intersection of Self-Correction and Trust in Language Models.

Satyapriya Krishna

CoRR, 2023

Are Large Language Models Post Hoc Explainers?

Nicholas Kroeger, Dan Ley, Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju

CoRR, 2023

On the Trade-offs between Adversarial Robustness and Actionable Explanations.

Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju

CoRR, 2023

Post Hoc Explanations of Language Models Can Improve Language Models.

Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten.

Satyapriya Krishna, Jiaqi Ma, Himabindu Lakkaraju

Proceedings of the International Conference on Machine Learning, 2023

2022

TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues.

Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh

CoRR, 2022

Rethinking Stability for Attribution-based Explanations.

Chirag Agarwal, Nari Johnson, Martin Pawelczyk, Satyapriya Krishna, Eshika Saxena, Marinka Zitnik, Himabindu Lakkaraju

CoRR, 2022

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective.

Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, Himabindu Lakkaraju

CoRR, 2022

OpenXAI: Towards a Transparent Evaluation of Model Explanations.

Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Measuring Fairness of Text Classifiers via Prediction Sensitivity.

Satyapriya Krishna, Rahul Gupta, Apurv Verma, Jwala Dhamala, Yada Pruksachatkun, Kai-Wei Chang

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal.

Umang Gupta, Jwala Dhamala, Varun Kumar, Apurv Verma, Yada Pruksachatkun, Satyapriya Krishna, Rahul Gupta, Kai-Wei Chang, Greg Ver Steeg, Aram Galstyan

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, 2022

2021

Grounding Complex Navigational Instructions Using Scene Graphs.

Michiel de Jong, Satyapriya Krishna, Anuva Agarwal

CoRR, 2021

BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation.

Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta

Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Towards Realistic Single-Task Continuous Learning Research for NER.

Justin Payan, Yuval Merhav, He Xie, Satyapriya Krishna, Anil Ramakrishna, Mukund Sridhar, Rahul Gupta

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

ADePT: Auto-encoder based Differentially Private Text Transformation.

Satyapriya Krishna, Rahul Gupta, Christophe Dupuy

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021

Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification.

Yada Pruksachatkun, Satyapriya Krishna, Jwala Dhamala, Rahul Gupta, Kai-Wei Chang

Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021

2020

Towards classification parity across cohorts.

Aarsh Patel, Rahul Gupta, Mukund Harakere, Satyapriya Krishna, Aman Alok, Peng Liu

CoRR, 2020

2019

FineText: Text Classification via Attention-based Language Model Fine-tuning.

Yunzhe Tao, Saurabh Gupta, Satyapriya Krishna, Xiong Zhou, Orchid Majumder, Vineet Khare

CoRR, 2019