Summarizing Source Code using a Neural Attention Model. - Srinivasan Iyer 001; Ioannis Konstas; Alvin Cheung; Luke Zettlemoyer (ACL (1), 2016)

Learning a Neural Semantic Parser from User Feedback. - Srinivasan Iyer 001; Ioannis Konstas; Alvin Cheung; Jayant Krishnamurthy; Luke Zettlemoyer (ACL (1), 2017)

Neural AMR: Sequence-to-Sequence Models for Parsing and Generation. - Ioannis Konstas; Srinivasan Iyer 001; Mark Yatskar; Yejin Choi 001; Luke Zettlemoyer (ACL (1), 2017)

Neural Semantic Parsing. - Matt Gardner 001; Pradeep Dasigi; Srinivasan Iyer 001; Alane Suhr; Luke Zettlemoyer (ACL (5), 2018)

Mapping Language to Code in Programmatic Context. - Srinivasan Iyer 001; Ioannis Konstas; Alvin Cheung; Luke Zettlemoyer (EMNLP, 2018)

Learning to Map Context-Dependent Sentences to Executable Formal Queries. - Alane Suhr; Srinivasan Iyer 001; Yoav Artzi (NAACL-HLT, 2018)

JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation. - Rajas Agashe; Srinivasan Iyer 001; Luke Zettlemoyer (EMNLP/IJCNLP (1), 2019)

Learning Programmatic Idioms for Scalable Semantic Parsing. - Srinivasan Iyer 001; Alvin Cheung; Luke Zettlemoyer (EMNLP/IJCNLP (1), 2019)

Learning to Map Natural Language to General Purpose Source Code. - Srinivasan Iyer 001 (2019)

Efficient One-Pass End-to-End Entity Linking for Questions. - Belinda Z. Li; Sewon Min; Srinivasan Iyer 001; Yashar Mehdad; Wen-tau Yih (EMNLP (1), 2020)

DeLighT: Very Deep and Light-weight Transformer. - Sachin Mehta; Marjan Ghazvininejad; Srinivasan Iyer 001; Luke Zettlemoyer; Hannaneh Hajishirzi (CoRR, 2020)

RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering. - Srinivasan Iyer 001; Sewon Min; Yashar Mehdad; Wen-tau Yih (CoRR, 2020)

Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA. - Ana Valeria Gonzalez; Gagan Bansal; Angela Fan; Robin Jia; Yashar Mehdad; Srinivasan Iyer 001 (CoRR, 2020)

Do Explanations Help Users Detect Errors in Open-Domain QA? An Evaluation of Spoken vs. Visual Explanations. - Ana Valeria Gonzalez; Gagan Bansal; Angela Fan; Yashar Mehdad; Robin Jia; Srinivasan Iyer 001 (ACL/IJCNLP (Findings), 2021)

FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation. - Kushal Lakhotia; Bhargavi Paranjape; Asish Ghoshal; Scott Yih; Yashar Mehdad; Srini Iyer 001 (EMNLP (1), 2021)

DeLighT: Deep and Light-weight Transformer. - Sachin Mehta; Marjan Ghazvininejad; Srinivasan Iyer 001; Luke Zettlemoyer; Hannaneh Hajishirzi (ICLR, 2021)

Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval. - Wenhan Xiong; Xiang Lorraine Li; Srini Iyer 001; Jingfei Du; Patrick Lewis 001; William Yang Wang; Yashar Mehdad; Scott Yih; Sebastian Riedel 001; Douwe Kiela; Barlas Oguz (ICLR, 2021)

RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering. - Srinivasan Iyer 001; Sewon Min; Yashar Mehdad; Wen-tau Yih (NAACL-HLT, 2021)

Multi-Perspective Abstractive Answer Summarization. - Alexander R. Fabbri; Xiaojian Wu; Srini Iyer 001; Mona T. Diab (CoRR, 2021)

EASE: Extractive-Abstractive Summarization with Explanations. - Haoran Li 007; Arash Einolghozati; Srinivasan Iyer 001; Bhargavi Paranjape; Yashar Mehdad; Sonal Gupta; Marjan Ghazvininejad (CoRR, 2021)

Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs. - Peter Hase; Mona T. Diab; Asli Celikyilmaz; Xian Li 003; Zornitsa Kozareva; Veselin Stoyanov; Mohit Bansal; Srinivasan Iyer 001 (CoRR, 2021)

Efficient Large Scale Language Modeling with Mixtures of Experts. - Mikel Artetxe; Shruti Bhosale; Naman Goyal 001; Todor Mihaylov; Myle Ott; Sam Shleifer; Xi Victoria Lin; Jingfei Du; Srinivasan Iyer 001; Ramakanth Pasunuru; Giri Anantharaman; Xian Li 003; et al. (CoRR, 2021)

ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection. - Badr AlKhamissi; Faisal Ladhak; Srini Iyer 001; Veselin Stoyanov; Zornitsa Kozareva; Xian Li 003; Pascale Fung; Lambert Mathias; Asli Celikyilmaz; Mona T. Diab (EMNLP, 2022)

Efficient Large Scale Language Modeling with Mixtures of Experts. - Mikel Artetxe; Shruti Bhosale; Naman Goyal 001; Todor Mihaylov; Myle Ott; Sam Shleifer; Xi Victoria Lin; Jingfei Du; Srinivasan Iyer 001; Ramakanth Pasunuru; Giridharan Anantharaman; Xian Li 003; et al. (EMNLP, 2022)

Improving In-Context Few-Shot Learning via Self-Supervised Training. - Mingda Chen; Jingfei Du; Ramakanth Pasunuru; Todor Mihaylov; Srini Iyer 001; Veselin Stoyanov; Zornitsa Kozareva (NAACL-HLT, 2022)

AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization. - Alexander R. Fabbri; Xiaojian Wu; Srini Iyer 001; Haoran Li 007; Mona T. Diab (NAACL-HLT, 2022)

QUASER: Question Answering with Scalable Extractive Rationalization. - Asish Ghoshal; Srinivasan Iyer 001; Bhargavi Paranjape; Kushal Lakhotia; Scott Wen-tau Yih; Yashar Mehdad (SIGIR, 2022)

OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization. - Srinivasan Iyer 001; Xi Victoria Lin; Ramakanth Pasunuru; Todor Mihaylov; Daniel Simig; Ping Yu; Kurt Shuster 001; Tianlu Wang; Qing Liu; Punit Singh Koura; Xian Li 003; Brian O'Horo; et al. (CoRR, 2022)

Complementary Explanations for Effective In-Context Learning. - Xi Ye; Srinivasan Iyer 001; Asli Celikyilmaz; Veselin Stoyanov; Greg Durrett; Ramakanth Pasunuru (ACL (Findings), 2023)

Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models. - Peter Hase; Mona T. Diab; Asli Celikyilmaz; Xian Li 003; Zornitsa Kozareva; Veselin Stoyanov; Mohit Bansal; Srinivasan Iyer 001 (EACL, 2023)

Demystifying Prompts in Language Models via Perplexity Estimation. - Hila Gonen; Srini Iyer 001; Terra Blevins; Noah A. Smith; Luke Zettlemoyer (EMNLP (Findings), 2023)

LEVER: Learning to Verify Language-to-Code Generation with Execution. - Ansong Ni; Srini Iyer 001; Dragomir Radev; Veselin Stoyanov; Wen-Tau Yih; Sida I. Wang; Xi Victoria Lin (ICML, 2023)

LIMA: Less Is More for Alignment. - Chunting Zhou; Pengfei Liu; Puxin Xu; Srinivasan Iyer 001; Jiao Sun; Yuning Mao; Xuezhe Ma; Avia Efrat; Ping Yu; Lili Yu; Susan Zhang; Gargi Ghosh; et al. (NeurIPS, 2023)

Instruction-tuned Language Models are Better Knowledge Learners. - Zhengbao Jiang; Zhiqing Sun; Weijia Shi; Pedro Rodríguez 001; Chunting Zhou; Graham Neubig; Xi Victoria Lin; Wen-tau Yih; Srini Iyer 001 (ACL (1), 2024)

ACUEval: Fine-grained Hallucination Evaluation and Correction for Abstractive Summarization. - David Wan; Koustuv Sinha; Srini Iyer 001; Asli Celikyilmaz; Mohit Bansal; Ramakanth Pasunuru (ACL (Findings), 2024)

MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts. - Xi Victoria Lin; Akshat Shrivastava; Liang Luo; Srinivasan Iyer 001; Mike Lewis; Gargi Ghosh; Luke Zettlemoyer; Armen Aghajanyan (CoRR, 2024)

Byte Latent Transformer: Patches Scale Better Than Tokens. - Artidoro Pagnoni; Ramakanth Pasunuru; Pedro Rodríguez 001; John Nguyen; Benjamin Muller; Margaret Li; Chunting Zhou; Lili Yu; Jason E. Weston; Luke Zettlemoyer; Gargi Ghosh; Mike Lewis; et al. (ACL (1), 2025)

Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models. - Weixin Liang; Lili Yu; Liang Luo; Srini Iyer 001; Ning Dong; Chunting Zhou; Gargi Ghosh; Mike Lewis; Wen-tau Yih; Luke Zettlemoyer; Xi Victoria Lin (Trans. Mach. Learn. Res., 2025)

ASTRO: Teaching Language Models to Reason by Reflecting and Backtracking In-Context. - Joongwon Kim; Anirudh Goyal; Liang Tan; Hannaneh Hajishirzi; Srinivasan Iyer 001; Tianlu Wang (CoRR, 2025)