Award Winning Papers
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Abstract Visual Reasoning with Tangram Shapes
Anya Ji, Noriyuki Kojima, N. Rush, Alane Suhr, Wai Keen Vong, Robert D. Hawkins, Yoav ArtziEMNLP • 2022We 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…Best Long Paper AwardCONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation
Abhilasha Ravichander, Matt Gardner, Ana MarasovićEMNLP • 2022 The full power of human language-based communication cannot be realized without negation. All human languages have some form of negation. Despite this, negation remains a challenging phenomenon for current natural language understanding systems. To facilitate…ProcTHOR: Large-Scale Embodied AI Using Procedural Generation
Matt Deitke, Eli VanderBilt, Alvaro Herrasti, Luca Weihs, Jordi Salvador, Kiana Ehsani, Winson Han, Eric Kolve, Ali Farhadi, Aniruddha Kembhavi, Roozbeh MottaghiNeurIPS • 2022 Massive datasets and high-capacity models have driven many recent advancements in computer vision and natural language understanding. This work presents a platform to enable similar success stories in Embodied AI. We propose ProcTHOR, a framework for…Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig SchmidtCVPR • 2022Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve…Best Paper FinalistNeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
Ximing Lu, S. Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, Yejin ChoiNAACL • 2022The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constraints, however, requires foresight to plan ahead feasible future paths. Drawing…Best Paper AwardUnderstanding Dataset Difficulty with 𝒱-Usable Information
Kawin Ethayarajh, Yejin Choi, and Swabha SwayamdiptaICML • 2022 Estimating the difficulty of a dataset typically involves comparing state-of-the-art models to humans; the bigger the performance gap, the harder the dataset is said to be. However, this comparison provides little understanding of how difficult each instance…Hallett‐Mossop Rime Splintering Dims Cumulus Clouds Over the Southern Ocean: New Insight From Nudged Global Storm‐Resolving Simulations
R. Atlas, C. Bretherton, M. Khairoutdinov, P. BlosseyAGU Advances • 2022 In clouds containing both liquid and ice with temperatures between −3°C and −8°C, liquid droplets collide with large ice crystals, freeze, and shatter, producing a plethora of small ice splinters. This process, known as Hallett‐Mossop rime splintering, and…Correcting Coarse-Grid Weather and Climate Models by Machine Learning From Global Storm-Resolving Simulations
Bretherton, C. S., B. Henn, A. Kwa, N. D. Brenowitz, O. Watt-Meyer, J. McGibbon, W. A. Perkins, S. K. Clark, and L. HarrisJournal of Advances in Modeling Earth Systems • 2022 Global atmospheric `storm-resolving' models with horizontal grid spacing of less than 5~km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse-grid…