Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU. - Olga Golovneva; Charith Peris (ICON, 2020)

Using multiple ASR hypotheses to boost i18n NLU performance. - Charith Peris; Gokmen Oz; Khadige Abboud; Venkata sai Varada; Prashan Wanigasekara; Haidar Khan (ICON, 2020)

Knowledge Distillation Transfer Sets and their Impact on Downstream NLU Tasks. - Charith Peris; Lizhen Tan; Thomas Gueudré; Turan Gojayev; Pan Wei; Gokmen Oz (EMNLP (Industry Track), 2022)

Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems. - Jack FitzGerald; Shankar Ananthakrishnan; Konstantine Arkoudas; Davide Bernardi; Abhishek Bhagia; Claudio Delli Bovi; Jin Cao 003; Rakesh Chada; Amit Chauhan; Luoxin Chen; Anurag Dwarakanath; Satyam Dwivedi; et al. (KDD, 2022)

Differentially Private Decoding in Large Language Models. - Jimit Majmudar; Christophe Dupuy; Charith Peris; Sami Smaili; Rahul Gupta 001; Richard S. Zemel (CoRR, 2022)

Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems. - Jack FitzGerald; Shankar Ananthakrishnan; Konstantine Arkoudas; Davide Bernardi; Abhishek Bhagia; Claudio Delli Bovi; Jin Cao 003; Rakesh Chada; Amit Chauhan; Luoxin Chen; Anurag Dwarakanath; Satyam Dwivedi; et al. (CoRR, 2022)

AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model. - Saleh Soltan; Shankar Ananthakrishnan; Jack FitzGerald; Rahul Gupta 001; Wael Hamza; Haidar Khan; Charith Peris; Stephen Rawls; Andy Rosenbaum; Anna Rumshisky; Chandana Satya Prakash; Mukund Sridhar; et al. (CoRR, 2022)

The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition. - Christopher Hench; Charith Peris; Jack FitzGerald; Kay Rottmann (CoRR, 2022)

MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages. - Jack FitzGerald; Christopher Hench; Charith Peris; Scott Mackie; Kay Rottmann; Ana Sanchez; Aaron Nash; Liam Urbach; Vishesh Kakarala; Richa Singh; Swetha Ranganath; Laurie Crist; et al. (ACL (1), 2023)

Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning. - Mustafa Özdayi; Charith Peris; Jack FitzGerald; Christophe Dupuy; Jimit Majmudar; Haidar Khan; Rahil Parikh; Rahul Gupta 001 (ACL (2), 2023)

Coordinated Replay Sample Selection for Continual Federated Learning. - Jack Good; Jimit Majmudar; Christophe Dupuy; Jixuan Wang; Charith Peris; Clement Chung; Richard S. Zemel; Rahul Gupta 001 (EMNLP (Industry Track), 2023)

Incorporating Fairness in Large Scale NLU Systems. - Rahul Gupta 001; Lisa Bauer; Kai-Wei Chang; Jwala Dhamala; Aram Galstyan; Palash Goyal; Qian Hu; Avni Khatri; Rohit Parimi; Charith Peris; Apurv Verma; Richard S. Zemel; et al. (WSDM, 2023)

Privacy in the Time of Language Models. - Charith Peris; Christophe Dupuy; Jimit Majmudar; Rahil Parikh; Sami Smaili; Richard S. Zemel; Rahul Gupta 001 (WSDM, 2023)

Holistic Survey of Privacy and Fairness in Machine Learning. - Sina Shaham; Arash Hajisafi; Minh K. Quan; Dinh C. Nguyen; Bhaskar Krishnamachari; Charith Peris; Gabriel Ghinita; Cyrus Shahabi; Pubudu N. Pathirana (CoRR, 2023)

Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs. - Elan Markowitz; Anil Ramakrishna; Jwala Dhamala; Ninareh Mehrabi; Charith Peris; Rahul Gupta 001; Kai-Wei Chang; Aram Galstyan (ACL (1), 2024)

Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification. - Tao Meng; Ninareh Mehrabi; Palash Goyal; Anil Ramakrishna; Aram Galstyan; Richard S. Zemel; Kai-Wei Chang; Rahul Gupta 001; Charith Peris (EMNLP (Findings), 2024)

Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains. - Krithika Ramesh; Nupoor Gandhi; Pulkit Madaan; Lisa Bauer; Charith Peris; Anjalie Field (EMNLP (Findings), 2024)

The steerability of large language models toward data-driven personas. - Junyi Li; Charith Peris; Ninareh Mehrabi; Palash Goyal; Kai-Wei Chang; Aram Galstyan; Richard S. Zemel; Rahul Gupta 001 (NAACL-HLT, 2024)

Partial Federated Learning. - Tiantian Feng; Anil Ramakrishna; Jimit Majmudar; Charith Peris; Jixuan Wang; Clement Chung; Richard S. Zemel; Morteza Ziyadi; Rahul Gupta 001 (CoRR, 2024)

Towards Safety Reasoning in LLMs: AI-agentic Deliberation for Policy-embedded CoT Data Creation. - Tharindu Kumarage; Ninareh Mehrabi; Anil Ramakrishna; Xinyan Zhao; Richard S. Zemel; Kai-Wei Chang; Aram Galstyan; Rahul Gupta 001; Charith Peris (ACL (Findings), 2025)

K-Edit: Language Model Editing with Contextual Knowledge Awareness. - Elan Markowitz; Anil Ramakrishna; Ninareh Mehrabi; Charith Peris; Rahul Gupta 001; Kai-Wei Chang; Aram Galstyan (CoRR, 2025)

Privacy and Fairness in Machine Learning: A Survey. - Sina Shaham; Arash Hajisafi; Minh K. Quan; Dinh C. Nguyen; Bhaskar Krishnamachari; Charith Peris; Gabriel Ghinita; Cyrus Shahabi; Pubudu N. Pathirana (IEEE Trans. Artif. Intell., 2025)