Monolingual sentence matching for text simplification

Yonghui Huang, Yunhui Li, Yi Luan

This work improves monolingual sentence alignment for text simplification, specifically for text in standard and simple Wikipedia. We introduce a convolutional neural network structure to model similarity between two sentences. Due to the limitation of available parallel corpora, the model is trained in a semi-supervised way, by using the output of a knowledge-based high performance aligning system. We apply the resulting similarity score to rescore the knowledge-based output, and adapt the model by a small hand-aligned dataset. Experiments show that both rescoring and adaptation improve the performance of knowledge-based method.

Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

Yi Luan, Luheng He, Mari Ostendorf, Hannaneh Hajishirzi

We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called Scientific Information Extractor (SciIE) for with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.

Scientific Relation Extraction with Selectively Incorporated Concept Embeddings

Yi Luan, Mari Ostendorf, Hannaneh Hajishirzi

This paper describes our submission for the SemEval 2018 Task 7 shared task on semantic relation extraction and classification in scientific papers. We extend the end-to-end relation extraction model of (Miwa and Bansal) with enhancements such as a character-level encoding attention mechanism on selecting pretrained concept candidate embeddings. Our official submission ranked the second in relation classification task (Subtask 1.1 and Subtask 2 Senerio 2), and the first in the relation extraction task (Subtask 2 Scenario 1).

Information Extraction from Scientific Literature for Method Recommendation

Yi Luan

As a research community grows, more and more papers are published each year. As a result there is increasing demand for improved methods for finding relevant papers, automatically understanding the key ideas and recommending potential methods for a target problem. Despite advances in search engines, it is still hard to identify new technologies according to a researcher's need. Due to the large variety of domains and extremely limited annotated resources, there has been relatively little work on leveraging natural language processing in scientific recommendation. In this proposal, we aim at making scientific recommendations by extracting scientific terms from a large collection of scientific papers and organizing the terms into a knowledge graph. In preliminary work, we trained a scientific term extractor using a small amount of annotated data and obtained state-of-the-art performance by leveraging large amount of unannotated papers through applying multiple semi-supervised approaches. We propose to construct a knowledge graph in a way that can make minimal use of hand annotated data, using only the extracted terms, unsupervised relational signals such as co-occurrence, and structural external resources such as Wikipedia. Latent relations between scientific terms can be learned from the graph. Recommendations will be made through graph inference for both observed and unobserved relational pairs.

A General Framework for Information Extraction using Dynamic Span Graphs

Yi Luan, Dave Wadden, Luheng He, Amy Shah, Mari Ostendorf, Hannaneh Hajishirzi

We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs. The graphs are constructed by selecting the most confident entity spans and linking these nodes with confidence-weighted relation types and coreferences. The dynamic span graph allows coreference and relation type confidences to propagate through the graph to iteratively refine the span representations. This is unlike previous multi-task frameworks for information extraction in which the only interaction between tasks is in the shared first-layer LSTM. Our framework significantly outperforms the state-of-the-art on multiple information extraction tasks across multiple datasets reflecting different domains. We further observe that the span enumeration approach is good at detecting nested span entities, with significant F1 score improvement on the ACE dataset.

Sparse, Dense, and Attentional Representations for Text Retrieval

Yi Luan, Jacob Eisenstein, Kristina Toutanova, Michael Collins

Dual encoders perform retrieval by encoding documents and queries into dense lowdimensional vectors, scoring each document by its inner product with the query. We investigate the capacity of this architecture relative to sparse bag-of-words models and attentional neural networks. Using both theoretical and empirical analysis, we establish connections between the encoding dimension, the margin between gold and lower-ranked documents, and the document length, suggesting limitations in the capacity of fixed-length encodings to support precise retrieval of long documents. Building on these insights, we propose a simple neural model that combines the efficiency of dual encoders with some of the expressiveness of more costly attentional architectures, and explore sparse-dense hybrids to capitalize on the precision of sparse retrieval. These models outperform strong alternatives in large-scale retrieval.

Scientific Information Extraction with Semi-supervised Neural Tagging

Yi Luan, Mari Ostendorf, Hannaneh Hajishirzi

This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and introduce semi-supervised methods to a neural tagging model, which builds on recent advances in named entity recognition. Since annotated training data is scarce in this domain, we introduce a graph-based semi-supervised algorithm together with a data selection scheme to leverage unannotated articles. Both inductive and transductive semi-supervised learning strategies outperform state-of-the-art information extraction performance on the 2017 SemEval Task 10 ScienceIE task.

LSTM based Conversation Models

Yi Luan, Yangfeng Ji, Mari Ostendorf

In this paper, we present a conversational model that incorporates both context and participant role for two-party conversations. Different architectures are explored for integrating participant role and context information into a Long Short-term Memory (LSTM) language model. The conversational model can function as a language model or a language generation model. Experiments on the Ubuntu Dialog Corpus show that our model can capture multiple turn interaction between participants. The proposed method outperforms a traditional LSTM model as measured by language model perplexity and response ranking. Generated responses show characteristic differences between the two participant roles.

Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models

Yi Luan, Chris Brockett, Bill Dolan, Jianfeng Gao, Michel Galley

Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training. This paper addresses the problem by proposing a multi-task learning approach to training neural conversation models that leverages both conversation data across speakers and other types of data pertaining to the speaker and speaker roles to be modeled. Experiments show that our approach leads to significant improvements over baseline model quality, generating responses that capture more precisely speakers' traits and speaking styles. The model offers the benefits of being algorithmically simple and easy to implement, and not relying on large quantities of data representing specific individual speakers.

Contextualized Representations Using Textual Encyclopedic Knowledge

Mandar Joshi, Kenton Lee, Yi Luan, Kristina Toutanova

We present a method to represent input texts by contextualizing them jointly with dynamically retrieved textual encyclopedic background knowledge from multiple documents. We apply our method to reading comprehension tasks by encoding questions and passages together with background sentences about the entities they mention. We show that integrating background knowledge from text is effective for tasks focusing on factual reasoning and allows direct reuse of powerful pretrained BERT-style encoders. Moreover, knowledge integration can be further improved with suitable pretraining via a self-supervised masked language model objective over words in background-augmented input text. On TriviaQA, our approach obtains improvements of 1.6 to 3.1 F1 over comparable RoBERTa models which do not integrate background knowledge dynamically. On MRQA, a large collection of diverse QA datasets, we see consistent gains in-domain along with large improvements out-of-domain on BioASQ (2.1 to 4.2 F1), TextbookQA (1.6 to 2.0 F1), and DuoRC (1.1 to 2.0 F1).

ASQA: Factoid Questions Meet Long-Form Answers

Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, Ming-Wei Chang

An abundance of datasets and availability of reliable evaluation metrics have resulted in strong progress in factoid question answering (QA). This progress, however, does not easily transfer to the task of long-form QA, where the goal is to answer questions that require in-depth explanations. The hurdles include (i) a lack of high-quality data, and (ii) the absence of a well-defined notion of the answer's quality. In this work, we address these problems by (i) releasing a novel dataset and a task that we call ASQA (Answer Summaries for Questions which are Ambiguous); and (ii) proposing a reliable metric for measuring performance on ASQA. Our task focuses on factoid questions that are ambiguous, that is, have different correct answers depending on interpretation. Answers to ambiguous questions should synthesize factual information from multiple sources into a long-form summary that resolves the ambiguity. In contrast to existing long-form QA tasks (such as ELI5), ASQA admits a clear notion of correctness: a user faced with a good summary should be able to answer different interpretations of the original ambiguous question. We use this notion of correctness to define an automated metric of performance for ASQA. Our analysis demonstrates an agreement between this metric and human judgments, and reveals a considerable gap between human performance and strong baselines.

CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning

Zeqiu Wu, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf, Gaurav Singh Tomar

Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each question needs to be interpreted within the dialogue context. Moreover, it can be expensive to re-train well-established retrievers such as search engines that are originally developed for non-conversational queries. To facilitate their use, we develop a query rewriting model CONQRR that rewrites a conversational question in the context into a standalone question. It is trained with a novel reward function to directly optimize towards retrieval using reinforcement learning and can be adapted to any off-the-shelf retriever. CONQRR achieves state-of-the-art results on a recent open-domain CQA dataset containing conversations from three different sources, and is effective for two different off-the-shelf retrievers. Our extensive analysis also shows the robustness of CONQRR to out-of-domain dialogues as well as to zero query rewriting supervision.

Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?

Yang Chen, Hexiang Hu, Yi Luan, Haitian Sun, Soravit Changpinyo, Alan Ritter, Ming-Wei Chang

Pre-trained vision and language models have demonstrated state-of-the-art capabilities over existing tasks involving images and texts, including visual question answering. However, it remains unclear whether these models possess the capability to answer questions that are not only querying visual content but knowledge-intensive and information-seeking. In this study, we introduce InfoSeek, a visual question answering dataset tailored for information-seeking questions that cannot be answered with only common sense knowledge. Using InfoSeek, we analyze various pre-trained visual question answering models and gain insights into their characteristics. Our findings reveal that state-of-the-art pre-trained multi-modal models (e.g., PaLI-X, BLIP2, etc.) face challenges in answering visual information-seeking questions, but fine-tuning on the InfoSeek dataset elicits models to use fine-grained knowledge that was learned during their pre-training. Furthermore, we show that accurate visual entity recognition can be used to improve performance on InfoSeek by retrieving relevant documents, showing a significant space for improvement.

Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?

Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Sébastien M. R. Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu

Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. Leveraging LCLMs' ability to natively ingest and process entire corpora of information offers numerous advantages. It enhances user-friendliness by eliminating the need for specialized knowledge of tools, provides robust end-to-end modeling that minimizes cascading errors in complex pipelines, and allows for the application of sophisticated prompting techniques across the entire system. To assess this paradigm shift, we introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks. However, LCLMs still face challenges in areas like compositional reasoning that are required in SQL-like tasks. Notably, prompting strategies significantly influence performance, emphasizing the need for continued research as context lengths grow. Overall, LOFT provides a rigorous testing ground for LCLMs, showcasing their potential to supplant existing paradigms and tackle novel tasks as model capabilities scale.

MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions

Kai Zhang, Yi Luan, Hexiang Hu, Kenton Lee, Siyuan Qiao, Wenhu Chen, Yu Su, Ming-Wei Chang

Image retrieval, i.e., finding desired images given a reference image, inherently encompasses rich, multi-faceted search intents that are difficult to capture solely using image-based measures. Recent works leverage text instructions to allow users to more freely express their search intents. However, they primarily focus on image pairs that are visually similar and/or can be characterized by a small set of pre-defined relations. The core thesis of this paper is that text instructions can enable retrieving images with richer relations beyond visual similarity. To show this, we introduce MagicLens, a series of self-supervised image retrieval models that support open-ended instructions. MagicLens is built on a key novel insight: image pairs that naturally occur on the same web pages contain a wide range of implicit relations (e.g., inside view of), and we can bring those implicit relations explicit by synthesizing instructions via foundation models. Trained on 36.7M (query image, instruction, target image) triplets with rich semantic relations mined from the web, MagicLens achieves results comparable with or better than prior best on eight benchmarks of various image retrieval tasks, while maintaining high parameter efficiency with a significantly smaller model size. Additional human analyses on a 1.4M-image unseen corpus further demonstrate the diversity of search intents supported by MagicLens. Code and models are publicly available at https://open-vision-language.github.io/MagicLens/.

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, Luke Marris, Sam Petulla, Colin Gaffney, Asaf Aharoni, Nathan Lintz, Tiago Cardal Pais, Henrik Jacobsson, Idan Szpektor, Nan-Jiang Jiang, Krishna Haridasan, Ahmed Omran, Nikunj Saunshi, Dara Bahri, Gaurav Mishra, Eric Chu, Toby Boyd, Brad Hekman, Aaron Parisi, Chaoyi Zhang, Kornraphop Kawintiranon, Tania Bedrax-Weiss, Oliver Wang, Ya Xu, Ollie Purkiss, Uri Mendlovic, Ilaï Deutel, Nam Nguyen, Adam Langley, Flip Korn, Lucia Rossazza, Alexandre Ramé, Sagar Waghmare, Helen Miller, Nathan Byrd, Ashrith Sheshan, Raia Hadsell, Sangnie Bhardwaj, Pawel Janus, Tero Rissa, Dan Horgan, Alvin Abdagic, Lior Belenki, James Allingham, Anima Singh, Theo Guidroz, Srivatsan Srinivasan, Herman Schmit, Kristen Chiafullo, Andre Elisseeff, Nilpa Jha, Prateek Kolhar, Leonard Berrada, Frank Ding, Xiance Si, Shrestha Basu Mallick, Franz Och, Sofia Erell, Eric Ni, Tejasi Latkar, Sherry Yang, Petar Sirkovic, Ziqiang Feng, Robert Leland, Rachel Hornung, Gang Wu, Charles Blundell, Hamidreza Alvari, Po-Sen Huang, Cathy Yip, Sanja Deur, Li Liu, Gabriela Surita, Pablo Duque, Dima Damen, Johnson Jia, Arthur Guez, Markus Mircea, Animesh Sinha, Alberto Magni, Paweł Stradomski, Tal Marian, Vlado Galić, Wenhu Chen, Hisham Husain, Achintya Singhal, Dominik Grewe, François-Xavier Aubet, Shuang Song, Lorenzo Blanco, Leland Rechis, Lewis Ho, Rich Munoz, Kelvin Zheng, Jessica Hamrick, Kevin Mather, Hagai Taitelbaum, Eliza Rutherford, Yun Lei, Kuangyuan Chen, Anand Shukla, Erica Moreira, Eric Doi, Berivan Isik, Nir Shabat, Dominika Rogozińska, Kashyap Kolipaka, Jason Chang, Eugen Vušak, Srinivasan Venkatachary, Shadi Noghabi, Tarun Bharti, Younghoon Jun, Aleksandr Zaks, Simon Green, Jeshwanth Challagundla, William Wong, Muqthar Mohammad, Dean Hirsch, Yong Cheng, Iftekhar Naim, Lev Proleev, Damien Vincent, Aayush Singh, Maxim Krikun, Dilip Krishnan, Zoubin Ghahramani, Aviel Atias, Rajeev Aggarwal, Christo Kirov, Dimitrios Vytiniotis, Christy Koh, Alexandra Chronopoulou, Pawan Dogra, Vlad-Doru Ion, Gladys Tyen, Jason Lee, Felix Weissenberger, Trevor Strohman, Ashwin Balakrishna, Jack Rae, Marko Velic, Raoul de Liedekerke, Oded Elyada, Wentao Yuan, Canoee Liu, Lior Shani, Sergey Kishchenko, Bea Alessio, Yandong Li, Richard Song, Sam Kwei, Orion Jankowski, Aneesh Pappu, Youhei Namiki, Yenai Ma, Nilesh Tripuraneni, Colin Cherry, Marissa Ikonomidis, Yu-Cheng Ling, Colin Ji, Beka Westberg, Auriel Wright, Da Yu, David Parkinson, Swaroop Ramaswamy, Jerome Connor, Soheil Hassas Yeganeh, Snchit Grover, George Kenwright, Lubo Litchev, Chris Apps, Alex Tomala, Felix Halim, Alex Castro-Ros, Zefei Li, Anudhyan Boral, Pauline Sho, Michal Yarom, Eric Malmi, David Klinghoffer, Rebecca Lin, Alan Ansell, Pradeep Kumar S, Shubin Zhao, Siqi Zuo, Adam Santoro, Heng-Tze Cheng, Solomon Demmessie, Yuchi Liu, Nicole Brichtova, Allie Culp, Nathaniel Braun, Dan Graur, Will Ng, Nikhil Mehta, Aaron Phillips, Patrik Sundberg, Varun Godbole, Fangyu Liu, Yash Katariya, David Rim, Mojtaba Seyedhosseini, Sean Ammirati, Jonas Valfridsson, Mahan Malihi, Timothy Knight, Andeep Toor, Thomas Lampe, Abe Ittycheriah, Lewis Chiang, Chak Yeung, Alexandre Fréchette, Jinmeng Rao, Huisheng Wang, Himanshu Srivastava, Richard Zhang, Rocky Rhodes, Ariel Brand, Dean Weesner, Ilya Figotin, Felix Gimeno, Rachana Fellinger, Pierre Marcenac, José Leal, Eyal Marcus, Victor Cotruta, Rodrigo Cabrera, Sheryl Luo, Dan Garrette, Vera Axelrod, Sorin Baltateanu, David Barker, Dongkai Chen, Horia Toma, Ben Ingram, Jason Riesa, Chinmay Kulkarni, Yujing Zhang, Hongbin Liu, Chao Wang, Martin Polacek, Will Wu, Kai Hui, Adrian N Reyes, Yi Su, Megan Barnes, Ishaan Malhi, Anfal Siddiqui, Qixuan Feng, Mihai Damaschin, Daniele Pighin, Andreas Steiner, Samuel Yang, Ramya Sree Boppana, Simeon Ivanov, Arun Kandoor, Aditya Shah, Asier Mujika, Da Huang, Christopher A. Choquette-Choo, Mohak Patel, Tianhe Yu, Toni Creswell, Jerry, Liu, Catarina Barros, Yasaman Razeghi, Aurko Roy, Phil Culliton, Binbin Xiong, Jiaqi Pan, Thomas Strohmann, Tolly Powell, Babi Seal, Doug DeCarlo, Pranav Shyam, Kaan Katircioglu, Xuezhi Wang, Cassidy Hardin, Immanuel Odisho, Josef Broder, Oscar Chang, Arun Nair, Artem Shtefan, Maura O'Brien, Manu Agarwal, Sahitya Potluri, Siddharth Goyal, Amit Jhindal, Saksham Thakur, Yury Stuken, James Lyon, Kristina Toutanova, Fangxiaoyu Feng, Austin Wu, Ben Horn, Alek Wang, Alex Cullum, Gabe Taubman, Disha Shrivastava, Chongyang Shi, Hamish Tomlinson, Roma Patel, Tao Tu, Ada Maksutaj Oflazer, Francesco Pongetti, Mingyao Yang, Adrien Ali Taïga, Vincent Perot, Nuo Wang Pierse, Feng Han, Yoel Drori, Iñaki Iturrate, Ayan Chakrabarti, Legg Yeung, Dave Dopson, Yi-ting Chen, Apoorv Kulshreshtha, Tongfei Guo, Philip Pham, Tal Schuster, Junquan Chen, Alex Polozov, Jinwei Xing, Huanjie Zhou, Praneeth Kacham, Doron Kukliansky, Antoine Miech, Sergey Yaroshenko, Ed Chi, Sholto Douglas, Hongliang Fei, Mathieu Blondel, Preethi Myla, Lior Madmoni, Xing Wu, Daniel Keysers, Kristian Kjems, Isabela Albuquerque, Lijun Yu, Joel D'sa, Michelle Plantan, Vlad Ionescu, Jaume Sanchez Elias, Abhirut Gupta, Manish Reddy Vuyyuru, Fred Alcober, Tong Zhou, Kaiyang Ji, Florian Hartmann, Subha Puttagunta, Hugo Song, Ehsan Amid, Anca Stefanoiu, Andrew Lee, Paul Pucciarelli, Emma Wang, Amit Raul, Slav Petrov, Isaac Tian, Valentin Anklin, Nana Nti, Victor Gomes, Max Schumacher, Grace Vesom, Alex Panagopoulos, Konstantinos Bousmalis, Daniel Andor, Josh Jacob, Yuan Zhang, Bill Rosgen, Matija Kecman, Matthew Tung, Alexandra Belias, Noah Goodman, Paul Covington, Brian Wieder, Nikita Saxena, Elnaz Davoodi, Muhuan Huang, Sharath Maddineni, Vincent Roulet, Folawiyo Campbell-Ajala, Pier Giuseppe Sessa, Xintian, Wu, Guangda Lai, Paul Collins, Alex Haig, Vytenis Sakenas, Xiaowei Xu, Marissa Giustina, Laurent El Shafey, Pichi Charoenpanit, Shefali Garg, Joshua Ainslie, Boone Severson, Montse Gonzalez Arenas, Shreya Pathak, Sujee Rajayogam, Jie Feng, Michiel Bakker, Sheng Li, Nevan Wichers, Jamie Rogers, Xinyang Geng, Yeqing Li, Rolf Jagerman, Chao Jia, Nadav Olmert, David Sharon, Matthew Mauger, Sandeep Mariserla, Hongxu Ma, Megha Mohabey, Kyuyeun Kim, Alek Andreev, Scott Pollom, Juliette Love, Vihan Jain, Priyanka Agrawal, Yannick Schroecker, Alisa Fortin, Manfred Warmuth, Ji Liu, Andrew Leach, Irina Blok, Ganesh Poomal Girirajan, Roee Aharoni, Benigno Uria, Andrei Sozanschi, Dan Goldberg, Lucian Ionita, Marco Tulio Ribeiro, Martin Zlocha, Vighnesh Birodkar, Sami Lachgar, Liangzhe Yuan, Himadri Choudhury, Matt Ginsberg, Fei Zheng, Gregory Dibb, Emily Graves, Swachhand Lokhande, Gabriel Rasskin, George-Cristian Muraru, Corbin Quick, Sandeep Tata, Pierre Sermanet, Aditya Chawla, Itay Karo, Yan Wang, Susan Zhang, Orgad Keller, Anca Dragan, Guolong Su, Ian Chou, Xi Liu, Yiqing Tao, Shruthi Prabhakara, Marc Wilson, Ruibo Liu, Shibo Wang, Georgie Evans, David Du, Alfonso Castaño, Gautam Prasad, Mona El Mahdy, Sebastian Gerlach, Machel Reid, Jarrod Kahn, Amir Zait, Thanumalayan Sankaranarayana Pillai, Thatcher Ulrich, Guanyu Wang, Jan Wassenberg, Efrat Farkash, Kiran Yalasangi, Congchao Wang, Maria Bauza, Simon Bucher, Ting Liu, Jun Yan, Gary Leung, Vikas Sindhwani, Parker Barnes, Avi Singh, Ivan Jurin, Jichuan Chang, Niket Kumar Bhumihar, Sivan Eiger, Gui Citovsky, Ben Withbroe, Zhang Li, Siyang Xue, Niccolò Dal Santo, Georgi Stoyanov, Yves Raimond, Steven Zheng, Yilin Gao, Vít Listík, Sławek Kwasiborski, Rachel Saputro, Adnan Ozturel, Ganesh Mallya, Kushal Majmundar, Ross West, Paul Caron, Jinliang Wei, Lluis Castrejon, Sharad Vikram, Deepak Ramachandran, Nikhil Dhawan, Jiho Park, Sara Smoot, George van den Driessche, Yochai Blau, Chase Malik, Wei Liang, Roy Hirsch, Cicero Nogueira dos Santos, Eugene Weinstein, Aäron van den Oord, Sid Lall, Nicholas FitzGerald, Zixuan Jiang, Xuan Yang, Dale Webster, Ali Elqursh, Aedan Pope, Georges Rotival, David Raposo, Wanzheng Zhu, Jeff Dean, Sami Alabed, Dustin Tran, Arushi Gupta, Zach Gleicher, Jessica Austin, Edouard Rosseel, Megh Umekar, Dipanjan Das, Yinghao Sun, Kai Chen, Karolis Misiunas, Xiang Zhou, Yixian Di, Alyssa Loo, Josh Newlan, Bo Li, Vinay Ramasesh, Ying Xu, Alex Chen, Sudeep Gandhe, Radu Soricut, Nikita Gupta, Shuguang Hu, Seliem El-Sayed, Xavier Garcia, Idan Brusilovsky, Pu-Chin Chen, Andrew Bolt, Lu Huang, Alex Gurney, Zhiying Zhang, Alexander Pritzel, Jarek Wilkiewicz, Bryan Seybold, Bhargav Kanagal Shamanna, Felix Fischer, Josef Dean, Karan Gill, Ross Mcilroy, Abhishek Bhowmick, Jeremy Selier, Antoine Yang, Derek Cheng, Vladimir Magay, Jie Tan, Dhriti Varma, Christian Walder, Tomas Kocisky, Ryo Nakashima, Paul Natsev, Mike Kwong, Ionel Gog, Chiyuan Zhang, Sander Dieleman, Thomas Jimma, Andrey Ryabtsev, Siddhartha Brahma, David Steiner, Dayou Du, Ante Žužul, Mislav Žanić, Mukund Raghavachari, Willi Gierke, Zeyu Zheng, Dessie Petrova, Yann Dauphin, Yuchuan Liu, Ido Kessler, Steven Hand, Chris Duvarney, Seokhwan Kim, Hyo Lee, Léonard Hussenot, Jeffrey Hui, Josh Smith, Deepali Jain, Jiawei Xia, Gaurav Singh Tomar, Keyvan Amiri, Du Phan, Fabian Fuchs, Tobias Weyand, Nenad Tomasev, Alexandra Cordell, Xin Liu, Jonathan Mallinson, Pankaj Joshi, Andy Crawford, Arun Suggala, Steve Chien, Nick Fernando, Mariella Sanchez-Vargas, Duncan Williams, Phil Crone, Xiyang Luo, Igor Karpov, Jyn Shan, Terry Thurk, Robin Strudel, Paul Voigtlaender, Piyush Patil, Tim Dozat, Ali Khodaei, Sahil Singla, Piotr Ambroszczyk, Qiyin Wu, Yifan Chang, Brian Roark, Chaitra Hegde, Tianli Ding, Angelos Filos, Zhongru Wu, André Susano Pinto, Shuang Liu, Saarthak Khanna, Aditya Pandey, Siobhan Mcloughlin, Qiujia Li, Sam Haves, Allan Zhou, Elena Buchatskaya, Isabel Leal, Peter de Boursac, Nami Akazawa, Nina Anderson, Terry Chen, Krishna Somandepalli, Chen Liang, Sheela Goenka, Stephanie Winkler, Alexander Grushetsky, Yifan Ding, Jamie Smith, Fan Ye, Jordi Pont-Tuset, Eric Li, Ruichao Li, Tomer Golany, Dawid Wegner, Tao Jiang, Omer Barak, Yuan Shangguan, Eszter Vértes, Renee Wong, Jörg Bornschein, Alex Tudor, Michele Bevilacqua, Tom Schaul, Ankit Singh Rawat, Yang Zhao, Kyriakos Axiotis, Lei Meng, Cory McLean, Jonathan Lai, Jennifer Beattie, Nate Kushman, Yaxin Liu, Blair Kutzman, Fiona Lang, Jingchen Ye, Praneeth Netrapalli, Pushkar Mishra, Myriam Khan, Megha Goel, Rob Willoughby, David Tian, Honglei Zhuang, JD Chen, Zak Tsai, Tasos Kementsietsidis, Arjun Khare, James Keeling, Keyang Xu, Nathan Waters, Florent Altché, Ashok Popat, Bhavishya Mittal, David Saxton, Dalia El Badawy, Michael Mathieu, Zheng Zheng, Hao Zhou, Nishant Ranka, Richard Shin, Qingnan Duan, Tim Salimans, Ioana Mihailescu, Uri Shaham, Ming-Wei Chang, Yannis Assael, Nishanth Dikkala, Martin Izzard, Vincent Cohen-Addad, Cat Graves, Vlad Feinberg, Grace Chung, DJ Strouse, Danny Karmon, Sahand Sharifzadeh, Zoe Ashwood, Khiem Pham, Jon Blanton, Alex Vasiloff, Jarred Barber, Mark Geller, Aurick Zhou, Fedir Zubach, Tzu-Kuo Huang, Lei Zhang, Himanshu Gupta, Matt Young, Julia Proskurnia, Ronny Votel, Valentin Gabeur, Gabriel Barcik, Aditya Tripathi, Hongkun Yu, Geng Yan, Beer Changpinyo, Filip Pavetić, Amy Coyle, Yasuhisa Fujii, Jorge Gonzalez Mendez, Tianhao Zhou, Harish Rajamani, Blake Hechtman, Eddie Cao, Da-Cheng Juan, Yi-Xuan Tan, Valentin Dalibard, Yilun Du, Natalie Clay, Kaisheng Yao, Wenhao Jia, Dimple Vijaykumar, Yuxiang Zhou, Xinyi Bai, Wei-Chih Hung, Steven Pecht, Georgi Todorov, Nikhil Khadke, Pramod Gupta, Preethi Lahoti, Arnaud Autef, Karthik Duddu, James Lee-Thorp, Alexander Bykovsky, Tautvydas Misiunas, Sebastian Flennerhag, Santhosh Thangaraj, Jed McGiffin, Zack Nado, Markus Kunesch, Andreas Noever, Amir Hertz, Marco Liang, Victor Stone, Evan Palmer, Samira Daruki, Arijit Pramanik, Siim Põder, Austin Kyker, Mina Khan, Evgeny Sluzhaev, Marvin Ritter, Avraham Ruderman, Wenlei Zhou, Chirag Nagpal, Kiran Vodrahalli, George Necula, Paul Barham, Ellie Pavlick, Jay Hartford, Izhak Shafran, Long Zhao, Maciej Mikuła, Tom Eccles, Hidetoshi Shimokawa, Kanav Garg, Luke Vilnis, Hanwen Chen, Ilia Shumailov, Kuang-Huei Lee, Abdelrahman Abdelhamed, Meiyan Xie, Vered Cohen, Ester Hlavnova, Dan Malkin, Chawin Sitawarin, James Lottes, Pauline Coquinot, Tianli Yu, Sandeep Kumar, Jingwei Zhang, Aroma Mahendru, Zafarali Ahmed, James Martens, Tao Chen, Aviel Boag, Daiyi Peng, Coline Devin, Arseniy Klimovskiy, Mary Phuong, Danny Vainstein, Jin Xie, Bhuvana Ramabhadran, Nathan Howard, Xinxin Yu, Gitartha Goswami, Jingyu Cui, Sam Shleifer, Mario Pinto, Chih-Kuan Yeh, Ming-Hsuan Yang, Sara Javanmardi, Dan Ethier, Chace Lee, Jordi Orbay, Suyog Kotecha, Carla Bromberg, Pete Shaw, James Thornton, Adi Gerzi Rosenthal, Shane Gu, Matt Thomas, Ian Gemp, Aditya Ayyar, Asahi Ushio, Aarush Selvan, Joel Wee, Chenxi Liu, Maryam Majzoubi, Weiren Yu, Jake Abernethy, Tyler Liechty, Renke Pan, Hoang Nguyen, Qiong, Hu, Sarah Perrin, Abhinav Arora, Emily Pitler, Weiyi 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Abhanshu Sharma, Yongqin Xian, Sungyong Seo, Claire Cui, Elena Pochernina, Cip Baetu, Krzysztof Jastrzębski, Mimi Ly, Mohamed Elhawaty, Dan Suh, Eren Sezener, Pidong Wang, Nancy Yuen, George Tucker, Jiahao Cai, Zuguang Yang, Cindy Wang, Alex Muzio, Hai Qian, Jae Yoo, Derek Lockhart, Kevin R. McKee, Mandy Guo, Malika Mehrotra, Artur Mendonça, Sanket Vaibhav Mehta, Sherry Ben, Chetan Tekur, Jiaqi Mu, Muye Zhu, Victoria Krakovna, Hongrae Lee, AJ Maschinot, Sébastien Cevey, HyunJeong Choe, Aijun Bai, Hansa Srinivasan, Derek Gasaway, Nick Young, Patrick Siegler, Dan Holtmann-Rice, Vihari Piratla, Kate Baumli, Roey Yogev, Alex Hofer, Hado van Hasselt, Svetlana Grant, Yuri Chervonyi, David Silver, Andrew Hogue, Ayushi Agarwal, Kathie Wang, Preeti Singh, Four Flynn, Josh Lipschultz, Robert David, Lizzetth Bellot, Yao-Yuan Yang, Long Le, Filippo Graziano, Kate Olszewska, Kevin Hui, Akanksha Maurya, Nikos Parotsidis, Weijie Chen, Tayo Oguntebi, Joe Kelley, Anirudh Baddepudi, Johannes Mauerer, Gregory Shaw, Alex Siegman, Lin Yang, Shravya Shetty, Subhrajit Roy, Yunting Song, Wojciech Stokowiec, Ryan Burnell, Omkar Savant, Robert Busa-Fekete, Jin Miao, Samrat Ghosh, Liam MacDermed, Phillip Lippe, Mikhail Dektiarev, Zach Behrman, Fabian Mentzer, Kelvin Nguyen, Meng Wei, Siddharth Verma, Chris Knutsen, Sudeep Dasari, Zhipeng Yan, Petr Mitrichev, Xingyu Wang, Virat Shejwalkar, Jacob Austin, Srinivas Sunkara, Navneet Potti, Yan Virin, Christian Wright, Gaël Liu, Oriana Riva, Etienne Pot, Greg Kochanski, Quoc Le, Gargi Balasubramaniam, Arka Dhar, Yuguo Liao, Adam Bloniarz, Divyansh Shukla, Elizabeth Cole, Jong Lee, Sheng Zhang, Sushant Kafle, Siddharth Vashishtha, Parsa Mahmoudieh, Grace Chen, Raphael Hoffmann, Pranesh Srinivasan, Agustin Dal Lago, Yoav Ben Shalom, Zi Wang, Michael Elabd, Anuj Sharma, Junhyuk Oh, Suraj Kothawade, Maigo Le, Marianne Monteiro, Shentao Yang, Kaiz Alarakyia, Robert Geirhos, Diana Mincu, Håvard Garnes, Hayato Kobayashi, Soroosh Mariooryad, Kacper Krasowiak, Zhixin, Lai, Shibl Mourad, Mingqiu Wang, Fan Bu, Ophir Aharoni, Guanjie Chen, Abhimanyu Goyal, Vadim Zubov, Ankur Bapna, Elahe Dabir, Nisarg Kothari, Kay Lamerigts, Nicola De Cao, Jeremy Shar, Christopher Yew, Nitish Kulkarni, Dre Mahaarachchi, Mandar Joshi, Zhenhai Zhu, Jared Lichtarge, Yichao Zhou, Hannah Muckenhirn, Vittorio Selo, Oriol Vinyals, Peter Chen, Anthony Brohan, Vaibhav Mehta, Sarah Cogan, Ruth Wang, Ty Geri, Wei-Jen Ko, Wei Chen, Fabio Viola, Keshav Shivam, Lisa Wang, Madeleine Clare Elish, Raluca Ada Popa, Sébastien Pereira, Jianqiao Liu, Raphael Koster, Donnie Kim, Gufeng Zhang, Sayna Ebrahimi, Partha Talukdar, Yanyan Zheng, Petra Poklukar, Ales Mikhalap, Dale Johnson, Anitha Vijayakumar, Mark Omernick, Matt Dibb, Ayush Dubey, Qiong Hu, Apurv Suman, Vaibhav Aggarwal, Ilya Kornakov, Fei Xia, Wing Lowe, Alexey Kolganov, Ted Xiao, Vitaly Nikolaev, Steven Hemingray, Bonnie Li, Joana Iljazi, Mikołaj Rybiński, Ballie Sandhu, Peggy Lu, Thang Luong, Rodolphe Jenatton, Vineetha Govindaraj, Hui, Li, Gabriel Dulac-Arnold, Wonpyo Park, Henry Wang, Abhinit Modi, Jean Pouget-Abadie, Kristina Greller, Rahul Gupta, Robert Berry, Prajit Ramachandran, Jinyu Xie, Liam McCafferty, Jianling Wang, Kilol Gupta, Hyeontaek Lim, Blaž Bratanič, Andy Brock, Ilia Akolzin, Jim Sproch, Dan Karliner, Duhyeon Kim, Adrian Goedeckemeyer, Noam Shazeer, Cordelia Schmid, Daniele Calandriello, Parul Bhatia, Krzysztof Choromanski, Ceslee Montgomery, Dheeru Dua, Ana Ramalho, Helen King, Yue Gao, Lynn Nguyen, David Lindner, Divya Pitta, Oleaser Johnson, Khalid Salama, Diego Ardila, Michael Han, Erin Farnese, Seth Odoom, Ziyue Wang, Xiangzhuo Ding, Norman Rink, Ray Smith, Harshal Tushar Lehri, Eden Cohen, Neera Vats, Tong He, Parthasarathy Gopavarapu, Adam Paszke, Miteyan Patel, Wouter Van Gansbeke, Lucia Loher, Luis Castro, Maria Voitovich, Tamara von Glehn, Nelson George, Simon Niklaus, Zach Eaton-Rosen, Nemanja Rakićević, Erik Jue, Sagi Perel, Carrie Zhang, Yuval Bahat, Angéline Pouget, Zhi Xing, Fantine Huot, Ashish Shenoy, Taylor Bos, Vincent Coriou, Bryan Richter, Natasha Noy, Yaqing Wang, Santiago Ontanon, Siyang Qin, Gleb Makarchuk, Demis Hassabis, Zhuowan Li, Mandar Sharma, Kumaran Venkatesan, Iurii Kemaev, Roxanne Daniel, Shiyu Huang, Saloni Shah, Octavio Ponce, Warren, Chen, Manaal Faruqui, Jialin Wu, Slavica Andačić, Szabolcs Payrits, Daniel McDuff, Tom Hume, Yuan Cao, MH Tessler, Qingze Wang, Yinan Wang, Ivor Rendulic, Eirikur Agustsson, Matthew Johnson, Tanya Lando, Andrew Howard, Sri Gayatri Sundara Padmanabhan, Mayank Daswani, Andrea Banino, Michael Kilgore, Jonathan Heek, Ziwei Ji, Alvaro Caceres, Conglong Li, Nora Kassner, Alexey Vlaskin, Zeyu Liu, Alex Grills, Yanhan Hou, Roykrong Sukkerd, Gowoon Cheon, Nishita Shetty, Larisa Markeeva, Piotr Stanczyk, Tejas Iyer, Yuan Gong, Shawn Gao, Keerthana Gopalakrishnan, Tim Blyth, Malcolm Reynolds, Avishkar Bhoopchand, Misha Bilenko, Dero Gharibian, Vicky Zayats, Aleksandra Faust, Abhinav Singh, Min Ma, Hongyang Jiao, Sudheendra Vijayanarasimhan, Lora Aroyo, Vikas Yadav, Sarah Chakera, Ashwin Kakarla, Vilobh Meshram, Karol Gregor, Gabriela Botea, Evan Senter, Dawei Jia, Geza Kovacs, Neha Sharma, Sebastien Baur, Kai Kang, Yifan He, Lin Zhuo, Marija Kostelac, Itay Laish, Songyou Peng, Louis O'Bryan, Daniel Kasenberg, Girish Ramchandra Rao, Edouard Leurent, Biao Zhang, Sage Stevens, Ana Salazar, Ye Zhang, Ivan Lobov, Jake Walker, Allen Porter, Morgan Redshaw, Han Ke, Abhishek Rao, Alex Lee, Hoi Lam, Michael Moffitt, Jaeyoun Kim, Siyuan Qiao, Terry Koo, Robert Dadashi, Xinying Song, Mukund Sundararajan, Peng Xu, Chizu Kawamoto, Yan Zhong, Clara Barbu, Apoorv Reddy, Mauro Verzetti, Leon Li, George Papamakarios, Hanna Klimczak-Plucińska, Mary Cassin, Koray Kavukcuoglu, Rigel Swavely, Alain Vaucher, Jeffrey Zhao, Ross Hemsley, Michael Tschannen, Heming Ge, Gaurav Menghani, Yang Yu, Natalie Ha, Wei He, Xiao Wu, Maggie Song, Rachel Sterneck, Stefan Zinke, Dan A. Calian, Annie Marsden, Alejandro Cruzado Ruiz, Matteo Hessel, Almog Gueta, Benjamin Lee, Brian Farris, Manish Gupta, Yunjie Li, Mohammad Saleh, Vedant Misra, Kefan Xiao, Piermaria Mendolicchio, Gavin Buttimore, Varvara Krayvanova, Nigamaa Nayakanti, Matthew Wiethoff, Yash Pande, Azalia Mirhoseini, Ni Lao, Jasmine Liu, Yiqing Hua, Angie Chen, Yury Malkov, Dmitry Kalashnikov, Shubham Gupta, Kartik Audhkhasi, Yuexiang Zhai, Sudhindra Kopalle, Prateek Jain, Eran Ofek, Clemens Meyer, Khuslen Baatarsukh, Hana Strejček, Jun Qian, James Freedman, Ricardo Figueira, Michal Sokolik, Olivier Bachem, Raymond Lin, Dia Kharrat, Chris Hidey, Pingmei Xu, Dennis Duan, Yin Li, Muge Ersoy, Richard Everett, Kevin Cen, Rebeca Santamaria-Fernandez, Amir Taubenfeld, Ian Mackinnon, Linda Deng, Polina Zablotskaia, Shashank Viswanadha, Shivanker Goel, Damion Yates, Yunxiao Deng, Peter Choy, Mingqing Chen, Abhishek Sinha, Alex Mossin, Yiming Wang, Arthur Szlam, Susan Hao, Paul Kishan Rubenstein, Metin Toksoz-Exley, Miranda Aperghis, Yin Zhong, Junwhan Ahn, Michael Isard, Olivier Lacombe, Florian Luisier, Chrysovalantis Anastasiou, Yogesh Kalley, Utsav Prabhu, Emma Dunleavy, Shaan Bijwadia, Justin Mao-Jones, Kelly Chen, Rama Pasumarthi, Emily Wood, Adil Dostmohamed, Nate Hurley, Jiri Simsa, Alicia Parrish, Mantas Pajarskas, Matt Harvey, Ondrej Skopek, Yony Kochinski, Javier Rey, Verena Rieser, Denny Zhou, Sun Jae Lee, Trilok Acharya, Guowang Li, Joe Jiang, Xiaofan Zhang, Bryant Gipson, Ethan Mahintorabi, Marco Gelmi, Nima Khajehnouri, Angel Yeh, Kayi Lee, Loic Matthey, Leslie Baker, Trang Pham, Han Fu, Alex Pak, Prakhar Gupta, Cristina Vasconcelos, Adam Sadovsky, Brian Walker, Sissie Hsiao, Patrik Zochbauer, Andreea Marzoca, Noam Velan, Junhao Zeng, Gilles Baechler, Danny Driess, Divya Jain, Yanping Huang, Lizzie Tao, John Maggs, Nir Levine, Jon Schneider, Erika Gemzer, Samuel Petit, Shan Han, Zach Fisher, Dustin Zelle, Courtney Biles, Eugene Ie, Asya Fadeeva, Casper Liu, Juliana Vicente Franco, Adrian Collister, Hao Zhang, Renshen Wang, Ruizhe Zhao, Leandro Kieliger, Kurt Shuster, Rui Zhu, Boqing Gong, Lawrence Chan, Ruoxi Sun, Sujoy Basu, Roland Zimmermann, Jamie Hayes, Abhishek Bapna, Jasper Snoek, Weel Yang, Puranjay Datta, Jad Al Abdallah, Kevin Kilgour, Lu Li, SQ Mah, Yennie Jun, Morgane Rivière, Abhijit Karmarkar, Tammo Spalink, Tao Huang, Lucas Gonzalez, Duc-Hieu Tran, Averi Nowak, John Palowitch, Martin Chadwick, Ellie Talius, Harsh Mehta, Thibault Sellam, Philipp Fränken, Massimo Nicosia, Kyle He, Aditya Kini, David Amos, Sugato Basu, Harrison Jobe, Eleni Shaw, Qiantong Xu, Colin Evans, Daisuke Ikeda, Chaochao Yan, Larry Jin, Lun Wang, Sachin Yadav, Ilia Labzovsky, Ramesh Sampath, Ada Ma, Candice Schumann, Aditya Siddhant, Rohin Shah, John Youssef, Rishabh Agarwal, Natalie Dabney, Alessio Tonioni, Moran Ambar, Jing Li, Isabelle Guyon, Benny Li, David Soergel, Boya Fang, Georgi Karadzhov, Cristian Udrescu, Trieu Trinh, Vikas Raunak, Seb Noury, Dee Guo, Sonal Gupta, Mara Finkelstein, Denis Petek, Lihao Liang, Greg Billock, Pei Sun, David Wood, Yiwen Song, Xiaobin Yu, Tatiana Matejovicova, Regev Cohen, Kalyan Andra, David D'Ambrosio, Zhiwei Deng, Vincent Nallatamby, Ebrahim Songhori, Rumen Dangovski, Andrew Lampinen, Pankil Botadra, Adam Hillier, Jiawei Cao, Nagabhushan Baddi, Adhi Kuncoro, Toshihiro Yoshino, Ankit Bhagatwala, Marcáurelio Ranzato, Rylan Schaeffer, Tianlin Liu, Shuai Ye, Obaid Sarvana, John Nham, Chenkai Kuang, Isabel Gao, Jinoo Baek, Shubham Mittal, Ayzaan Wahid, Anita Gergely, Bin Ni, Josh Feldman, Carrie Muir, Pascal Lamblin, Wolfgang Macherey, Ethan Dyer, Logan Kilpatrick, Víctor Campos, Mukul Bhutani, Stanislav Fort, Yanif Ahmad, Aliaksei Severyn, Kleopatra Chatziprimou, Oleksandr Ferludin, Mason Dimarco, Aditya Kusupati, Joe Heyward, Dan Bahir, Kevin Villela, Katie Millican, Dror Marcus, Sanaz Bahargam, Caglar Unlu, Nicholas Roth, Zichuan Wei, Siddharth Gopal, Deepanway Ghoshal, Edward Lee, Sharon Lin, Jennie Lees, Dayeong Lee, Anahita Hosseini, Connie Fan, Seth Neel, Marcus Wu, Yasemin Altun, Honglong Cai, Enrique Piqueras, Josh Woodward, Alessandro Bissacco, Salem Haykal, Mahyar Bordbar, Prasha Sundaram, Sarah Hodkinson, Daniel Toyama, George Polovets, Austin Myers, Anu Sinha, Tomer Levinboim, Kashyap Krishnakumar, Rachita Chhaparia, Tatiana Sholokhova, Nitesh Bharadwaj Gundavarapu, Ganesh Jawahar, Haroon Qureshi, Jieru Hu, Nikola Momchev, Matthew Rahtz, Renjie Wu, Aishwarya P S, Kedar Dhamdhere, Meiqi Guo, Umang Gupta, Ali Eslami, Mariano Schain, Michiel Blokzijl, David Welling, Dave Orr, Levent Bolelli, Nicolas Perez-Nieves, Mikhail Sirotenko, Aman Prasad, Arjun Kar, Borja De Balle Pigem, Tayfun Terzi, Gellért Weisz, Dipankar Ghosh, Aditi Mavalankar, Dhruv Madeka, Kaspar Daugaard, Hartwig Adam, Viraj Shah, Dana Berman, Maggie Tran, Steven Baker, Ewa Andrejczuk, Grishma Chole, Ganna Raboshchuk, Mahdi Mirzazadeh, Thais Kagohara, Shimu Wu, Christian Schallhart, Bernett Orlando, Chen Wang, Alban Rrustemi, Hao Xiong, Hao Liu, Arpi Vezer, Nolan Ramsden, Shuo-yiin Chang, Sidharth Mudgal, Yan Li, Nino Vieillard, Yedid Hoshen, Farooq Ahmad, Ambrose Slone, Amy Hua, Natan Potikha, Mirko Rossini, Jon Stritar, Sushant Prakash, Zifeng Wang, Xuanyi Dong, Alireza Nazari, Efrat Nehoran, Kaan Tekelioglu, Yinxiao Li, Kartikeya Badola, Tom Funkhouser, Yuanzhen Li, Varun Yerram, Ramya Ganeshan, Daniel Formoso, Karol Langner, Tian Shi, Huijian Li, Yumeya Yamamori, Amayika Panda, Alaa Saade, Angelo Scorza Scarpati, Chris Breaux, CJ Carey, Zongwei Zhou, Cho-Jui Hsieh, Sophie Bridgers, Alena Butryna, Nishesh Gupta, Vaibhav Tulsyan, Sanghyun Woo, Evgenii Eltyshev, Will Grathwohl, Chanel Parks, Seth Benjamin, Rina Panigrahy, Shenil Dodhia, Daniel De Freitas, Chris Sauer, Will Song, Ferran Alet, Jackson Tolins, Cosmin Paduraru, Xingyi Zhou, Brian Albert, Zizhao Zhang, Lei Shu, Mudit Bansal, Sarah Nguyen, Amir Globerson, Owen Xiao, James Manyika, Tom Hennigan, Rong Rong, Josip Matak, Anton Bakalov, Ankur Sharma, Danila Sinopalnikov, Andrew Pierson, Stephen Roller, Geoff Brown, Mingcen Gao, Toshiyuki Fukuzawa, Amin Ghafouri, Kenny Vassigh, Iain Barr, Zhicheng Wang, Anna Korsun, Rajesh Jayaram, Lijie Ren, Tim Zaman, Samira Khan, Yana Lunts, Dan Deutsch, Dave Uthus, Nitzan Katz, Masha Samsikova, Amr Khalifa, Nikhil Sethi, Jiao Sun, Luming Tang, Uri Alon, Xianghong Luo, Dian Yu, Abhishek Nayyar, Bryce Petrini, Will Truong, Vincent Hellendoorn, Nikolai Chinaev, Chris Alberti, Wei Wang, Jingcao Hu, Vahab Mirrokni, Ananth Balashankar, Avia Aharon, Aahil Mehta, Ahmet Iscen, Joseph Kready, Lucas Manning, Anhad Mohananey, Yuankai Chen, Anshuman Tripathi, Allen Wu, Igor Petrovski, Dawsen Hwang, Martin Baeuml, Shreyas Chandrakaladharan, Yuan Liu, Rey Coaguila, Maxwell Chen, Sally Ma, Pouya Tafti, Susheel Tatineni, Terry Spitz, Jiayu Ye, Paul Vicol, Mihaela Rosca, Adrià Puigdomènech, Zohar Yahav, Sanjay Ghemawat, Hanzhao Lin, Phoebe Kirk, Zaid Nabulsi, Sergey Brin, Bernd Bohnet, Ken Caluwaerts, Aditya Srikanth Veerubhotla, Dan Zheng, Zihang Dai, Petre Petrov, Yichong Xu, Ramin Mehran, Zhuo Xu, Luisa Zintgraf, Jiho Choi, Spurthi Amba Hombaiah, Romal Thoppilan, Sashank Reddi, Lukasz Lew, Li Li, Kellie Webster, KP Sawhney, Lampros Lamprou, Siamak Shakeri, Mayank Lunayach, Jianmin Chen, Sumit Bagri, Alex Salcianu, Ying Chen, Yani Donchev, Charlotte Magister, Signe Nørly, Vitor Rodrigues, Tomas Izo, Hila Noga, Joe Zou, Thomas Köppe, Wenxuan Zhou, Kenton Lee, Xiangzhu Long, Danielle Eisenbud, Anthony Chen, Connor Schenck, Chi Ming To, Peilin Zhong, Emanuel Taropa, Minh Truong, Omer Levy, Danilo Martins, Zhiyuan Zhang, Christopher Semturs, Kelvin Zhang, Alex Yakubovich, Pol Moreno, Lara McConnaughey, Di Lu, Sam Redmond, Lotte Weerts, Yonatan Bitton, Tiziana Refice, Nicolas Lacasse, Arthur Conmy, Corentin Tallec, Julian Odell, Hannah Forbes-Pollard, Arkadiusz Socala, Jonathan Hoech, Pushmeet Kohli, Alanna Walton, Rui Wang, Mikita Sazanovich, Kexin Zhu, Andrei Kapishnikov, Rich Galt, Matthew Denton, Ben Murdoch, Caitlin Sikora, Kareem Mohamed, Wei Wei, Uri First, Tim McConnell, Luis C. Cobo, James Qin, Thi Avrahami, Daniel Balle, Yu Watanabe, Annie Louis, Adam Kraft, Setareh Ariafar, Yiming Gu, Eugénie Rives, Charles Yoon, Andrei Rusu, James Cobon-Kerr, Chris Hahn, Jiaming Luo, Yuvein, Zhu, Niharika Ahuja, Rodrigo Benenson, Raphaël Lopez Kaufman, Honglin Yu, Lloyd Hightower, Junlin Zhang, Darren Ni, Lisa Anne Hendricks, Gabby Wang, Gal Yona, Lalit Jain, Pablo Barrio, Surya Bhupatiraju, Siva Velusamy, Allan Dafoe, Sebastian Riedel, Tara Thomas, Zhe Yuan, Mathias Bellaiche, Sheena Panthaplackel, Klemen Kloboves, Sarthak Jauhari, Canfer Akbulut, Todor Davchev, Evgeny Gladchenko, David Madras, Aleksandr Chuklin, Tyrone Hill, Quan Yuan, Mukundan Madhavan, Luke Leonhard, Dylan Scandinaro, Qihang Chen, Ning Niu, Arthur Douillard, Bogdan Damoc, Yasumasa Onoe, Fabian Pedregosa, Fred Bertsch, Chas Leichner, Joseph Pagadora, Jonathan Malmaud, Sameera Ponda, Andy Twigg, Oleksii Duzhyi, Jingwei Shen, Miaosen Wang, Roopal Garg, Jing Chen, Utku Evci, Jonathan Lee, Leon Liu, Koji Kojima, Masa Yamaguchi, Arunkumar Rajendran, AJ Piergiovanni, Vinodh Kumar Rajendran, Marco Fornoni, Gabriel Ibagon, Harry Ragan, Sadh MNM Khan, John Blitzer, Andrew Bunner, Guan Sun, Takahiro Kosakai, Scott Lundberg, Ndidi Elue, Kelvin Guu, SK Park, Jane Park, Arunachalam Narayanaswamy, Chengda Wu, Jayaram Mudigonda, Trevor Cohn, Hairong Mu, Ravi Kumar, Laura Graesser, Yichi Zhang, Richard Killam, Vincent Zhuang, Mai Giménez, Wael Al Jishi, Ruy Ley-Wild, Alex Zhai, Kazuki Osawa, Diego Cedillo, Jialu Liu, Mayank Upadhyay, Marcin Sieniek, Roshan Sharma, Tom Paine, Anelia Angelova, Sravanti Addepalli, Carolina Parada, Kingshuk Majumder, Avery Lamp, Sanjiv Kumar, Xiang Deng, Artiom Myaskovsky, Tea Sabolić, Jeffrey Dudek, Sarah York, Félix de Chaumont Quitry, Jiazhong Nie, Dee Cattle, Alok Gunjan, Bilal Piot, Waleed Khawaja, Seojin Bang, Simon Wang, Siavash Khodadadeh, Raghavender R, Praynaa Rawlani, Richard Powell, Kevin Lee, Johannes Griesser, GS Oh, Cesar Magalhaes, Yujia Li, Simon Tokumine, Hadas Natalie Vogel, Dennis Hsu, Arturo BC, Disha Jindal, Matan Cohen, Zi Yang, Junwei Yuan, Dario de Cesare, Tony Bruguier, Jun Xu, Monica Roy, Alon Jacovi, Dan Belov, Rahul Arya, Phoenix Meadowlark, Shlomi Cohen-Ganor, Wenting Ye, Patrick Morris-Suzuki, Praseem Banzal, Gan Song, Pranavaraj Ponnuramu, Fred Zhang, George Scrivener, Salah Zaiem, Alif Raditya Rochman, Kehang Han, Badih Ghazi, Kate Lee, Shahar Drath, Daniel Suo, Antonious Girgis, Pradeep Shenoy, Duy Nguyen, Douglas Eck, Somit Gupta, Le Yan, Joao Carreira, Anmol Gulati, Ruoxin Sang, Daniil Mirylenka, Emma Cooney, Edward Chou, Mingyang Ling, Cindy Fan, Ben Coleman, Guilherme Tubone, Ravin Kumar, Jason Baldridge, Felix Hernandez-Campos, Angeliki Lazaridou, James Besley, Itay Yona, Neslihan Bulut, Quentin Wellens, AJ Pierigiovanni, Jasmine George, Richard Green, Pu Han, Connie Tao, Geoff Clark, Chong You, Abbas Abdolmaleki, Justin Fu, Tongzhou Chen, Ashwin Chaugule, Angad Chandorkar, Altaf Rahman, Will Thompson, Penporn Koanantakool, Mike Bernico, Jie Ren, Andrey Vlasov, Sergei Vassilvitskii, Maciej Kula, Yizhong Liang, Dahun Kim, Yangsibo Huang, Chengxi Ye, Dmitry Lepikhin, Wesley Helmholz

Entity, Relation, and Event Extraction with Contextualized Span Representations

David Wadden, Ulme Wennberg, Yi Luan, Hannaneh Hajishirzi

We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction. Our framework (called DyGIE++) accomplishes all tasks by enumerating, refining, and scoring text spans designed to capture local (within-sentence) and global (cross-sentence) context. Our framework achieves state-of-the-art results across all tasks, on four datasets from a variety of domains. We perform experiments comparing different techniques to construct span representations. Contextualized embeddings like BERT perform well at capturing relationships among entities in the same or adjacent sentences, while dynamic span graph updates model long-range cross-sentence relationships. For instance, propagating span representations via predicted coreference links can enable the model to disambiguate challenging entity mentions. Our code is publicly available at https://github.com/dwadden/dygiepp and can be easily adapted for new tasks or datasets.

Text Generation from Knowledge Graphs with Graph Transformers

Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, Hannaneh Hajishirzi

Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce. In this work, we address the problem of generating coherent multi-sentence texts from the output of an information extraction system, and in particular a knowledge graph. Graphical knowledge representations are ubiquitous in computing, but pose a significant challenge for text generation techniques due to their non-hierarchical nature, collapsing of long-distance dependencies, and structural variety. We introduce a novel graph transforming encoder which can leverage the relational structure of such knowledge graphs without imposing linearization or hierarchical constraints. Incorporated into an encoder-decoder setup, we provide an end-to-end trainable system for graph-to-text generation that we apply to the domain of scientific text. Automatic and human evaluations show that our technique produces more informative texts which exhibit better document structure than competitive encoder-decoder methods.

Large Dual Encoders Are Generalizable Retrievers

Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y. Zhao, Yi Luan, Keith B. Hall, Ming-Wei Chang, Yinfei Yang

It has been shown that dual encoders trained on one domain often fail to generalize to other domains for retrieval tasks. One widespread belief is that the bottleneck layer of a dual encoder, where the final score is simply a dot-product between a query vector and a passage vector, is too limited to make dual encoders an effective retrieval model for out-of-domain generalization. In this paper, we challenge this belief by scaling up the size of the dual encoder model {\em while keeping the bottleneck embedding size fixed.} With multi-stage training, surprisingly, scaling up the model size brings significant improvement on a variety of retrieval tasks, especially for out-of-domain generalization. Experimental results show that our dual encoders, \textbf{G}eneralizable \textbf{T}5-based dense \textbf{R}etrievers (GTR), outperform %ColBERT~\cite{khattab2020colbert} and existing sparse and dense retrievers on the BEIR dataset~\cite{thakur2021beir} significantly. Most surprisingly, our ablation study finds that GTR is very data efficient, as it only needs 10\% of MS Marco supervised data to achieve the best out-of-domain performance. All the GTR models are released at https://tfhub.dev/google/collections/gtr/1.

Gecko: Versatile Text Embeddings Distilled from Large Language Models

Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, Blair Chen, Daniel Cer, Jeremy R. Cole, Kai Hui, Michael Boratko, Rajvi Kapadia, Wen Ding, Yi Luan, Sai Meher Karthik Duddu, Gustavo Hernandez Abrego, Weiqiang Shi, Nithi Gupta, Aditya Kusupati, Prateek Jain, Siddhartha Reddy Jonnalagadda, Ming-Wei Chang, Iftekhar Naim

We present Gecko, a compact and versatile text embedding model. Gecko achieves strong retrieval performance by leveraging a key idea: distilling knowledge from large language models (LLMs) into a retriever. Our two-step distillation process begins with generating diverse, synthetic paired data using an LLM. Next, we further refine the data quality by retrieving a set of candidate passages for each query, and relabeling the positive and hard negative passages using the same LLM. The effectiveness of our approach is demonstrated by the compactness of the Gecko. On the Massive Text Embedding Benchmark (MTEB), Gecko with 256 embedding dimensions outperforms all existing entries with 768 embedding size. Gecko with 768 embedding dimensions achieves an average score of 66.31, competing with 7x larger models and 5x higher dimensional embeddings.