| BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation J Dhamala, T Sun, V Kumar, S Krishna, Y Pruksachatkun, KW Chang, ... ACM FAccT Conference 2021, 2021 | 363 | 2021 |
| The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective S Krishna, T Han, A Gu, J Pombra, S Jabbari, S Wu, H Lakkaraju Transactions on Machine Learning Research, 2024, 2024 | 247 | 2024 |
| Openxai: Towards a transparent evaluation of model explanations C Agarwal, S Krishna, E Saxena, M Pawelczyk, N Johnson, I Puri, M Zitnik, ... Advances in neural information processing systems 35, 15784-15799, 2022 | 172 | 2022 |
| Explaining machine learning models with interactive natural language conversations using TalkToModel D Slack, S Krishna, H Lakkaraju, S Singh Nature Machine Intelligence, 1-11, 2023 | 103* | 2023 |
| Black-Box Access is Insufficient for Rigorous AI Audits S Casper, C Ezell, C Siegmann, N Kolt, TL Curtis, B Bucknall, A Haupt, ... ACM FAccT Conference 2024, 2024 | 66* | 2024 |
| Post Hoc Explanations of Language Models Can Improve Language Models S Krishna, J Ma, D Slack, A Ghandeharioun, S Singh, H Lakkaraju Advances in Neural Information Processing Systems, 2023 36, 2023 | 58 | 2023 |
| Rethinking Stability for Attribution-based Explanations C Agarwal, N Johnson, M Pawelczyk, S Krishna, E Saxena, M Zitnik, ... ICLR 2022 Workshop on PAIR^2Struct: Privacy, Accountability …, 2022 | 56 | 2022 |
| Adept: Auto-encoder based differentially private text transformation S Krishna, R Gupta, C Dupuy Proceedings of the 16th Conference of the European Chapter of the …, 2021 | 49 | 2021 |
| Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal U Gupta, J Dhamala, V Kumar, A Verma, Y Pruksachatkun, S Krishna, ... Findings of the Association for Computational Linguistics: ACL 2022, 2022 | 47 | 2022 |
| Eagle and finch: Rwkv with matrix-valued states and dynamic recurrence B Peng, D Goldstein, Q Anthony, A Albalak, E Alcaide, S Biderman, ... arXiv preprint arXiv:2404.05892, 2024 | 37 | 2024 |
| Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification S Krishna*, Y Pruksachatkun*, J Dhamala, R Gupta, KW Chang Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021 | 35* | 2021 |
| Are Large Language Models Post Hoc Explainers? N Kroeger, D Ley, S Krishna, C Agarwal, H Lakkaraju arXiv preprint arXiv:2310.05797, 2023 | 30 | 2023 |
| Croissant: A Metadata Format for ML-Ready Datasets Published at the NeurIPS 2024 Datasets and Benchmark Track. A shorter …, 2024 | 27* | 2024 |
| Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten S Krishna, J Ma, H Lakkaraju The Fortieth International Conference on Machine Learning (ICML), 2023, 2023 | 14 | 2023 |
| Measuring Fairness of Text Classifiers via Prediction Sensitivity S Krishna, R Gupta, A Verma, J Dhamala, Y Pruksachatkun, KW Chang Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 10 | 2022 |
| Operationalizing a threat model for red-teaming large language models (llms) A Verma, S Krishna, S Gehrmann, M Seshadri, A Pradhan, T Ault, ... arXiv preprint arXiv:2407.14937, 2024 | 8 | 2024 |
| Understanding the Effects of Iterative Prompting on Truthfulness S Krishna, C Agarwal, H Lakkaraju Forty-first International Conference on Machine Learning, 2024, 2024 | 7 | 2024 |
| On the Intersection of Self-Correction and Trust in Language Models S Krishna arXiv preprint arXiv:2311.02801, 2023 | 6 | 2023 |
| Towards Realistic Single-Task Continuous Learning Research for NER J Payan, Y Merhav, H Xie, S Krishna, A Ramakrishna, M Sridhar, R Gupta Findings of the Association for Computational Linguistics: EMNLP 2021, 2021 | 6 | 2021 |
| On the Trade-offs between Adversarial Robustness and Actionable Explanations S Krishna, C Agarwal, H Lakkaraju 7th AAAI Conference on AI, Ethics, and Society, 2024 | 3* | 2024 |
Himabindu LakkarajuAssistant Professor, Harvard UniversityVerified email at seas.harvard.edu
