Sreedhar, Makesh Narsimhan

NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails

Rebedea, T., Dinu, R., Sreedhar, M. N., Parisien, C., & Cohen, J. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 220

Nemotron-4 340B Technical Report

Nvidia, :, Adler, B., Agarwal, N., Aithal, A., Anh, D. H., Bhattacharya, P., Brundyn, A., Casper, J., Catanzaro, B., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 66

AEGIS2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails

Ghosh, S., Varshney, P., Sreedhar, M. N., Padmakumar, A., Rebedea, T., Varghese, J. R., & Parisien, C. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 15

SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF

Dong, Y., Wang, Z., Sreedhar, M., Wu, X., & Kuchaiev, O. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Topic ModelingNatural Language Processing Techniques

Cited by 15

NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails

Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., & Cohen, J. (2023) · arXiv

Speech and dialogue systemsEthics and Social Impacts of AI

Cited by 8

HelpSteer2: Open-source dataset for training top-performing reward models

Wang, Z., Dong, Y., Delalleau, O., Zeng, J., Shen, G., Egert, D., Zhang, J. J., Sreedhar, M. N., & Kuchaiev, O. (2024) · arXiv

Reinforcement Learning in RoboticsTopic Modeling

Cited by 7

Single Sequence Prediction over Reasoning Graphs for Multi-hop QA

Ramesh, G., Sreedhar, M. N., & Hu, J. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingMultimodal Machine Learning Applications

Cited by 3

Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models

NVIDIA, :, Blakeman, A., Basant, A., Khattar, A., Renduchintala, A., Bercovich, A., Ficek, A., Bjorlin, A., Taghibakhshi, A., et al. (2025) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 0