Papers

Learn more about AI2's Lasting Impact Award
Viewing 41-50 of 221 papers
  • Statistical and Computational Guarantees for Influence Diagnostics

    Jillian Fisher, Lang Liu, Krishna Pillutla, Yejin Choi, Zaid HarchaouiarXiv2022 Influence diagnostics such as influence functions and approximate maximum influence perturbations are popular in machine learning and in AI domain applications. Influence diagnostics are powerful statistical tools to identify influential datapoints or subsets…
  • Abstract Visual Reasoning with Tangram Shapes

    Anya Ji, Noriyuki Kojima, N. Rush, Alane Suhr, Wai Keen Vong, Robert D. Hawkins, Yoav ArtziEMNLP2022
    Best Long Paper Award
    We introduce KiloGram, a resource for studying abstract visual reasoning in humans and machines. Drawing on the history of tangram puzzles as stimuli in cognitive science, we build a richly annotated dataset that, with > 1k distinct stimuli, is orders of…
  • Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

    Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, A. Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, I. Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Maitreya Patel, Kuntal Kumar Pal, M. Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Shailaja Keyur Sampat, Savan Doshi, S. Mishra, Sujan Reddy, Sumanta Patro, Tanay Dixit, Xudong Shen, Chitta Baral, Yejin Choi, Noah A. Smith, Hanna Hajishirzi, Daniel KhashabiEMNLP2022 How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce SUPER-NATURALINSTRUCTIONS, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our…
  • Twist Decoding: Diverse Generators Guide Each Other

    Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Hao Peng, Ximing Lu, Dragomir Radev, Yejin Choi, Noah A. SmithEMNLP2022 Natural language generation technology has recently seen remarkable progress with large-scale training, and many natural language applications are now built upon a wide range of generation models. Combining diverse models may lead to further progress, but…
  • WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation

    Alisa Liu, Swabha Swayamdipta, Noah A. Smith, Yejin ChoiFindings of EMNLP2022 A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. We introduce a novel approach for dataset creation based on worker and AI…
  • NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries?

    Saadia Gabriel, H. Palangi, Yejin ChoiarXiv2022 While a substantial body of prior work has explored adversarial example generation for natural language understanding tasks, these examples are often unrealistic and diverge from the real-world data distributions. In this work, we introduce a two-stage…
  • Quantifying the narrative flow of imagined versus autobiographical stories.

    Maarten Sap, A. Jafarpour, Yejin Choi, Noah A. Smith, J. Pennebaker, E. HorvitzProceedings of the National Academy of Sciences of the United States of America2022 Lifelong experiences and learned knowledge lead to shared expectations about how common situations tend to unfold. Such knowledge of narrative event flow enables people to weave together a story. However, comparable computational tools to evaluate the flow of…
  • Generating Sequences by Learning to Self-Correct

    S. Welleck, Ximing Lu, Peter West, Faeze Brahman, T. Shen, Daniel Khashabi, Yejin ChoiarXiv2022 Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesir-able content. Language models, whether fine-tuned or prompted with few-shot demonstrations…
  • Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE

    Yuling Gu, Yao Fu, Valentina Pyatkin, Ian Magnusson, Bhavana Dalvi Mishra, Peter ClarkEMNLP • The Third Workshop on Figurative Language Processing 2022 Figurative language (e.g., “he flew like the wind”) is challenging to understand, as it is hard to tell what implicit information is being conveyed from the surface form alone. We hypothesize that to perform this task well, the reader needs to mentally…
  • Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation

    Melanie Sclar, Peter West, Sachin Kumar, Yulia Tsvetkov, Yejin ChoiConference on Empirical Methods in Natural Language Processing2022 We present Referee, a novel framework for sentence summarization that can be trained reference-free (i.e., requiring no gold summaries for supervision), while allowing direct control for compression ratio. Our work is the first to demonstrate that reference…