Best Practices for Data-Efficient Modeling in NLG: How to Train Production-Ready Neural Models with Less Data. - Ankit Arun; Soumya Batra; Vikas Bhardwaj; Ashwini Challa; Pinar Donmez; Peyman Heidari; Hakan Inan; Shashank Jain; Anuj Kumar; Shawn Mei; Karthik Mohan; Michael White 001 (COLING (Industry), 2020)

Building Adaptive Acceptability Classifiers for Neural NLG. - Soumya Batra; Shashank Jain; Peyman Heidari; Ankit Arun; Catharine Youngs; Xintong Li; Pinar Donmez; Shawn Mei; Shiunzu Kuo; Vikas Bhardwaj; Anuj Kumar; Michael White 001 (EMNLP (1), 2021)

Getting to Production with Few-shot Natural Language Generation Models. - Peyman Heidari; Arash Einolghozati; Shashank Jain; Soumya Batra; Lee Callender; Ankit Arun; Shawn Mei; Sonal Gupta; Pinar Donmez; Vikas Bhardwaj; Anuj Kumar; Michael White 001 (SIGDIAL, 2021)

A Study on the Efficiency and Generalization of Light Hybrid Retrievers. - Man Luo 003; Shashank Jain; Anchit Gupta; Arash Einolghozati; Barlas Oguz; Debojeet Chatterjee; Xilun Chen 002; Chitta Baral; Peyman Heidari (ACL (2), 2023)

AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model. - Seungwhan Moon; Andrea Madotto; Zhaojiang Lin; Tushar Nagarajan; Matt Smith; Shashank Jain; Chun-Fu Yeh; Prakash Murugesan; Peyman Heidari; Yue Liu; Kavya Srinet; Babak Damavandi; et al. (EMNLP (Industry Track), 2024)