Question Answering through Transfer Learning from Large Fine-grained Supervision Data. - Sewon Min; Min Joon Seo; Hannaneh Hajishirzi (ACL (2), 2017)

Query-Reduction Networks for Question Answering. - Min Joon Seo; Sewon Min; Ali Farhadi; Hannaneh Hajishirzi (ICLR (Poster), 2017)

Efficient and Robust Question Answering from Minimal Context over Documents. - Sewon Min; Victor Zhong; Richard Socher; Caiming Xiong (ACL (1), 2018)

Neural Speed Reading via Skim-RNN. - Min Joon Seo; Sewon Min; Ali Farhadi; Hannaneh Hajishirzi (ICLR (Poster), 2018)

On Making Reading Comprehension More Comprehensive. - Matt Gardner 001; Jonathan Berant; Hannaneh Hajishirzi; Alon Talmor; Sewon Min (MRQA@EMNLP, 2019)

Compositional Questions Do Not Necessitate Multi-hop Reasoning. - Sewon Min; Eric Wallace; Sameer Singh 001; Matt Gardner 001; Hannaneh Hajishirzi; Luke Zettlemoyer (ACL (1), 2019)

Multi-hop Reading Comprehension through Question Decomposition and Rescoring. - Sewon Min; Victor Zhong; Luke Zettlemoyer; Hannaneh Hajishirzi (ACL (1), 2019)

A Discrete Hard EM Approach for Weakly Supervised Question Answering. - Sewon Min; Danqi Chen 001; Hannaneh Hajishirzi; Luke Zettlemoyer (EMNLP/IJCNLP (1), 2019)

Question Answering is a Format; When is it Useful? - Matt Gardner 001; Jonathan Berant; Hannaneh Hajishirzi; Alon Talmor; Sewon Min (CoRR, 2019)

Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering. - Sewon Min; Danqi Chen 001; Luke Zettlemoyer; Hannaneh Hajishirzi (CoRR, 2019)

Dense Passage Retrieval for Open-Domain Question Answering. - Vladimir Karpukhin; Barlas Oguz; Sewon Min; Patrick Lewis 001; Ledell Wu; Sergey Edunov; Danqi Chen 001; Wen-tau Yih (EMNLP (1), 2020)

UnifiedQA: Crossing Format Boundaries With a Single QA System. - Daniel Khashabi; Sewon Min; Tushar Khot; Ashish Sabharwal; Oyvind Tafjord; Peter Clark; Hannaneh Hajishirzi (EMNLP (Findings), 2020)

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)

AmbigQA: Answering Ambiguous Open-domain Questions. - Sewon Min; Julian Michael; Hannaneh Hajishirzi; Luke Zettlemoyer (EMNLP (1), 2020)

NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned. - Sewon Min; Jordan L. Boyd-Graber; Chris Alberti; Danqi Chen 001; Eunsol Choi; Michael Collins 001; Kelvin Guu; Hannaneh Hajishirzi; Kenton Lee; Jennimaria Palomaki; Colin Raffel; Adam Roberts; et al. (NeurIPS (Competition and Demos), 2020)

Dense Passage Retrieval for Open-Domain Question Answering. - Vladimir Karpukhin; Barlas Oguz; Sewon Min; Ledell Wu; Sergey Edunov; Danqi Chen 001; Wen-tau Yih (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)

Joint Passage Ranking for Diverse Multi-Answer Retrieval. - Sewon Min; Kenton Lee; Ming-Wei Chang; Kristina Toutanova; Hannaneh Hajishirzi (EMNLP (1), 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)

PROMPT WAYWARDNESS: The Curious Case of Discretized Interpretation of Continuous Prompts. - Daniel Khashabi; Shane Lyu; Sewon Min; Lianhui Qin; Kyle Richardson 001; Sameer Singh 001; Sean Welleck; Hannaneh Hajishirzi; Tushar Khot; Ashish Sabharwal; Yejin Choi 001 (CoRR, 2021)

Zero- and Few-Shot NLP with Pretrained Language Models. - Iz Beltagy; Arman Cohan; Robert L. Logan IV; Sewon Min; Sameer Singh 001 (ACL (tutorial), 2022)

Noisy Channel Language Model Prompting for Few-Shot Text Classification. - Sewon Min; Mike Lewis; Hannaneh Hajishirzi; Luke Zettlemoyer (ACL (1), 2022)

FaVIQ: FAct Verification from Information-seeking Questions. - Jungsoo Park; Sewon Min; Jaewoo Kang; Luke Zettlemoyer; Hannaneh Hajishirzi (ACL (1), 2022)

Exploring The Landscape of Distributional Robustness for Question Answering Models. - Anas Awadalla; Mitchell Wortsman; Gabriel Ilharco; Sewon Min; Ian Magnusson; Hannaneh Hajishirzi; Ludwig Schmidt (EMNLP (Findings), 2022)

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? - Sewon Min; Xinxi Lyu; Ari Holtzman; Mikel Artetxe; Mike Lewis; Hannaneh Hajishirzi; Luke Zettlemoyer (EMNLP, 2022)

Re-Examining Calibration: The Case of Question Answering. - Chenglei Si; Chen Zhao 009; Sewon Min; Jordan L. Boyd-Graber (EMNLP (Findings), 2022)

Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts. - Daniel Khashabi; Xinxi Lyu; Sewon Min; Lianhui Qin; Kyle Richardson 001; Sean Welleck; Hannaneh Hajishirzi; Tushar Khot; Ashish Sabharwal; Sameer Singh 001; Yejin Choi 001 (NAACL-HLT, 2022)

MetaICL: Learning to Learn In Context. - Sewon Min; Mike Lewis; Luke Zettlemoyer; Hannaneh Hajishirzi (NAACL-HLT, 2022)

Revisiting Calibration for Question Answering. - Chenglei Si; Chen Zhao 009; Sewon Min; Jordan L. Boyd-Graber (CoRR, 2022)

CREPE: Open-Domain Question Answering with False Presuppositions. - Xinyan Yu 001; Sewon Min; Luke Zettlemoyer; Hannaneh Hajishirzi (ACL (1), 2023)

Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations. - Xinxi Lyu; Sewon Min; Iz Beltagy; Luke Zettlemoyer; Hannaneh Hajishirzi (ACL (1), 2023)

Nonparametric Masked Language Modeling. - Sewon Min; Weijia Shi; Mike Lewis; Xilun Chen 002; Wen-tau Yih; Hannaneh Hajishirzi; Luke Zettlemoyer (ACL (Findings), 2023)

Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters. - Boshi Wang; Sewon Min; Xiang Deng 001; Jiaming Shen; You Wu 001; Luke Zettlemoyer; Huan Sun 001 (ACL (1), 2023)

Retrieval-based Language Models and Applications. - Akari Asai; Sewon Min; Zexuan Zhong; Danqi Chen 001 (ACL (tutorial), 2023)

FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation. - Sewon Min; Kalpesh Krishna; Xinxi Lyu; Mike Lewis; Wen-tau Yih; Pang Wei Koh; Mohit Iyyer; Luke Zettlemoyer; Hannaneh Hajishirzi (EMNLP, 2023)

Measuring and Narrowing the Compositionality Gap in Language Models. - Ofir Press; Muru Zhang; Sewon Min; Ludwig Schmidt; Noah A. Smith; Mike Lewis (EMNLP (Findings), 2023)

SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore. - Sewon Min; Suchin Gururangan; Eric Wallace; Hannaneh Hajishirzi; Noah A. Smith; Luke Zettlemoyer (CoRR, 2023)

InSCIt: Information-Seeking Conversations with Mixed-Initiative Interactions. - Zeqiu Wu; Ryu Parish; Hao Cheng 002; Sewon Min; Prithviraj Ammanabrolu; Mari Ostendorf; Hannaneh Hajishirzi (Trans. Assoc. Comput. Linguistics, 2023)

CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation. - Tong Chen; Akari Asai; Niloofar Mireshghallah; Sewon Min; James Grimmelmann; Yejin Choi 001; Hannaneh Hajishirzi; Luke Zettlemoyer; Pang Wei Koh (EMNLP, 2024)

BTR: Binary Token Representations for Efficient Retrieval Augmented Language Models. - Qingqing Cao; Sewon Min; Yizhong Wang; Hannaneh Hajishirzi (ICLR, 2024)

SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore. - Sewon Min; Suchin Gururangan; Eric Wallace; Weijia Shi; Hannaneh Hajishirzi; Noah A. Smith; Luke Zettlemoyer (ICLR, 2024)

In-Context Pretraining: Language Modeling Beyond Document Boundaries. - Weijia Shi; Sewon Min; Maria Lomeli; Chunting Zhou; Margaret Li; Xi Victoria Lin; Noah A. Smith; Luke Zettlemoyer; Wen-tau Yih; Mike Lewis (ICLR, 2024)

REPLUG: Retrieval-Augmented Black-Box Language Models. - Weijia Shi; Sewon Min; Michihiro Yasunaga; Minjoon Seo; Richard James 001; Mike Lewis; Luke Zettlemoyer; Wen-tau Yih (NAACL-HLT, 2024)

Scaling Retrieval-Based Language Models with a Trillion-Token Datastore. - Rulin Shao; Jacqueline He; Akari Asai; Weijia Shi; Tim Dettmers; Sewon Min; Luke Zettlemoyer; Pang Wei Koh (NeurIPS, 2024)

Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens. - Jiacheng Liu 010; Sewon Min; Luke Zettlemoyer; Yejin Choi 001; Hannaneh Hajishirzi (CoRR, 2024)

Do Membership Inference Attacks Work on Large Language Models? - Michael Duan; Anshuman Suri; Niloofar Mireshghallah; Sewon Min; Weijia Shi; Luke Zettlemoyer; Yulia Tsvetkov; Yejin Choi 001; David Evans 001; Hannaneh Hajishirzi (CoRR, 2024)

OLMoE: Open Mixture-of-Experts Language Models. - Niklas Muennighoff; Luca Soldaini; Dirk Groeneveld; Kyle Lo; Jacob Morrison; Sewon Min; Weijia Shi; Pete Walsh; Oyvind Tafjord; Nathan Lambert 001; Yuling Gu; Shane Arora; et al. (CoRR, 2024)

Rethinking Data Use in Large Language Models - Sewon Min (2024)

OLMoE: Open Mixture-of-Experts Language Models. - Niklas Muennighoff; Luca Soldaini; Dirk Groeneveld; Kyle Lo; Jacob Morrison; Sewon Min; Weijia Shi; Evan Pete Walsh; Oyvind Tafjord; Nathan Lambert 001; Yuling Gu; Shane Arora; et al. (ICLR, 2025)

Organize the Web: Constructing Domains Enhances Pre-Training Data Curation. - Alexander Wettig; Kyle Lo; Sewon Min; Hannaneh Hajishirzi; Danqi Chen 001; Luca Soldaini (CoRR, 2025)

OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens. - Jiacheng Liu 010; Taylor Blanton; Yanai Elazar; Sewon Min; Yen-Sung Chen; Arnavi Chheda-Kothary; Huy Tran; Byron Bischoff; Eric Marsh; Michael Schmitz 002; Cassidy Trier; Aaron Sarnat; et al. (CoRR, 2025)

Reasoning Models Can Be Effective Without Thinking. - Wenjie Ma; Jingxuan He; Charlie Snell; Tyler Griggs; Sewon Min; Matei Zaharia (CoRR, 2025)

ReasonIR: Training Retrievers for Reasoning Tasks. - Rulin Shao; Rui Qiao 006; Varsha Kishore; Niklas Muennighoff; Xi Victoria Lin; Daniela Rus; Bryan Kian Hsiang Low; Sewon Min; Wen-tau Yih; Pang Wei Koh; Luke Zettlemoyer (CoRR, 2025)

LEANN: A Low-Storage Vector Index. - Yichuan Wang; Shu Liu; Zhifei Li; Yongji Wu; Ziming Mao; Yilong Zhao; Xiao Yan; Zhiying Xu; Yang Zhou; Ion Stoica; Sewon Min; Matei Zaharia; et al. (CoRR, 2025)

Spurious Rewards: Rethinking Training Signals in RLVR. - Rulin Shao; Shuyue Stella Li; Rui Xin; Scott Geng; Yiping Wang; Sewoong Oh; Simon Shaolei Du; Nathan Lambert 001; Sewon Min; Ranjay Krishna; Yulia Tsvetkov; Hannaneh Hajishirzi; et al. (CoRR, 2025)

Frustratingly Simple Retrieval Improves Challenging, Reasoning-Intensive Benchmarks. - Xinxi Lyu; Michael Duan; Rulin Shao; Pang Wei Koh; Sewon Min (CoRR, 2025)

FlexOlmo: Open Language Models for Flexible Data Use. - Weijia Shi; Akshita Bhagia; Kevin Farhat; Niklas Muennighoff; Pete Walsh; Jacob Morrison; Dustin Schwenk; Shayne Longpre; Jake Poznanski; Allyson Ettinger; Daogao Liu; Margaret Li; et al. (CoRR, 2025)