| 2026 | ACL | Bridging the Culture Gap: A Framework for LLM-Driven Socio-Cultural Localization of Math Word Problems in Low-Resource Languages. | Israel Abebe Azime, Tadesse Destaw Belay, Dietrich Klakow, Philipp Slusallek, Anshuman Chhabra |
| 2025 | ACL | Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms. | Rajvardhan Oak, Muhammad Haroon, Claire Wonjeong Jo, Magdalena Wojcieszak, Anshuman Chhabra |
| 2025 | ICML | Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models. | Anshuman Chhabra, Bo Li, Jian Chen, Prasant Mohapatra, Hongfu Liu |
| 2025 | IJCNLP | "Whose Side Are You On?" Estimating Ideology of Political and News Content Using Large Language Models and Few-shot Demonstration Selection. | Muhammad Haroon, Magdalena Wojcieszak, Anshuman Chhabra |
| 2025 | IJCNLP | From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models. | Mahammed Kamruzzaman, Abdullah Al Monsur, Gene Louis Kim, Anshuman Chhabra |
| 2025 | NAACL | Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI Safety Moderation Classifiers. | Akshit Achara, Anshuman Chhabra |
| 2025 | NAACL | Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing. | Hadi Askari, Anshuman Chhabra, Muhao Chen, Prasant Mohapatra |
| 2025 | NeSy | Rethinking Reasoning in LLMs: Neuro-Symbolic Local RetoMaton Beyond CoT and ICL. | Rushitha Santhoshi Mamidala, Anshuman Chhabra, Ankur Mali |
| 2024 | ICLR | "What Data Benefits My Classifier?" Enhancing Model Performance and Interpretability through Influence-Based Data Selection. | Anshuman Chhabra, Peizhao Li, Prasant Mohapatra, Hongfu Liu |
| 2024 | NAACL | Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias. | Anshuman Chhabra, Hadi Askari, Prasant Mohapatra |
| 2023 | ICLR | Robust Fair Clustering: A Novel Fairness Attack and Defense Framework. | Anshuman Chhabra, Peizhao Li, Prasant Mohapatra, Hongfu Liu |
| 2022 | ICMLA | Fair Algorithms for Hierarchical Agglomerative Clustering. | Anshuman Chhabra, Prasant Mohapatra |
| 2021 | CCS | Moving Target Defense against Adversarial Machine Learning. | Anshuman Chhabra, Prasant Mohapatra |
| 2020 | AAAI | Suspicion-Free Adversarial Attacks on Clustering Algorithms. | Anshuman Chhabra, Abhishek Roy, Prasant Mohapatra |
| 2019 | SECON | A Machine Learning Approach Using Classifier Cascades for Optimal Routing in Opportunistic Internet of Things Networks. | Vidushi Vashishth, Anshuman Chhabra, Deepak Kumar Sharma |
| 2017 | CISS | SEIR: A Stackelberg game based approach for energy-aware and incentivized routing in selfish Opportunistic Networks. | Anshuman Chhabra, Vidushi Vashishth, Deepak Kumar Sharma |
| 2017 | CISS | A game theory based secure model against Black hole attacks in Opportunistic Networks. | Anshuman Chhabra, Vidushi Vashishth, Deepak Kumar Sharma |