Papers by Krishnaram Kenthapadi
On the Lack of Robust Interpretability of Neural Text Classifiers (2021.findings-acl)
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Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cedric Archambeau, Sanjiv Das, Krishnaram Kenthapadi
| Challenge: | Several models have been proposed to interpret models with feature-based interpretability methods. |
| Approach: | They propose to quantify the robustness of neural text classifiers by using two randomization tests to compare models with identical initializations. |
| Outcome: | The proposed methods show surprising deviations from expected behavior . the results raise questions about the extent of insights that practitioners may draw from interpretations. |
What’s in a Name? Reducing Bias in Bios without Access to Protected Attributes (N19-1)
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Alexey Romanov, Maria De-Arteaga, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Kalai
| Challenge: | Existing methods for mitigating bias in machine learning systems rely on access to protected attributes such as race, gender, or age. |
| Approach: | They propose a method for discouraging correlation between predicted probability of an individual’s true occupation and a word embedding of their name. |
| Outcome: | The proposed method reduces race and gender biases, with almost no reduction in the classifier’s overall true positive rate. |
Mastering the Craft of Data Synthesis for CodeLLMs (2025.naacl-long)
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Meng Chen, Philip Arthur, Qianyu Feng, Cong Duy Vu Hoang, Yu-Heng Hong, Mahdi Kazemi Moghaddam, Omid Nezami, Duc Thien Nguyen, Gioacchino Tangari, Duy Vu, Thanh Vu, Mark Johnson, Krishnaram Kenthapadi, Don Dharmasiri, Long Duong, Yuan-Fang Li
| Challenge: | Large language models (LLMs) have shown impressive performance in code understanding and generation. |
| Approach: | They propose a systematic review of large language models and their taxonomy and propose specialized LLMs for code-related tasks. |
| Outcome: | The proposed models have shown to be highly effective in coding tasks. |
RedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification (2025.acl-industry)
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Praphul Singh, Charlotte Dzialo, Jangwon Kim, Sumana Srivatsa, Irfan Bulu, Sri Gadde, Krishnaram Kenthapadi
| Challenge: | Existing de-identification methods suffer from recall errors, limited generalization, and inefficiencies, limiting their real-world applicability. |
| Approach: | They propose a multi-modal framework for de-identifying electronic health records using a retrieval-based entity relexicalization approach. |
| Outcome: | The proposed framework achieves competitive performance while optimizing token usage to reduce LLM costs. |