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Generated Knowledge Prompting for Commonsense Reasoning
Jiachen Liu, Alisa Liu, Ximing Lu, S. Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh HajishirziACL • 2022 Despite their ability to capture large amount of knowledge during pretraining, large-scale language models often benefit from incorporating external knowledge bases, especially on commonsense reasoning tasks. This motivates us to explore how we can best…Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets
Yuxiang Wu, Matt Gardner, Pontus Stenetorp, Pradeep DasigiACL • 2022 Natural language processing models often exploit spurious correlations between task-independent features and labels in datasets to perform well only within the distributions they are trained on, while not generalising to different task distributions. We…Generating Scientific Definitions with Controllable Complexity
Tal August, Katharina Reinecke, Noah A. SmithACL • 2022 Unfamiliar terminology and complex language can present barriers to understanding science. Natural language processing stands to help address these issues by automatically defining unfamiliar terms. We introduce a new task and dataset for defining scientific…Generating Scientific Claims for Zero-Shot Scientific Fact Checking
Dustin Wright, David Wadden, Kyle Lo, Bailey Kuehl, Arman Cohan, Isabelle Augenstein, Lucy Lu WangACL • 2022 Automated scientific fact checking is difficult due to the complexity of scientific language and a lack of significant amounts of training data, as annotation requires domain expertise. To address this challenge, we propose scientific claim generation, the…Hey AI, Can You Solve Complex Tasks by Talking to Agents?
Tushar Khot, Kyle Richardson, Daniel Khashabi, Ashish SabharwalFindings of ACL • 2022 Humans often solve complex problems by interacting (in natural language) with existing agents, such as AI assistants, that can solve simpler sub-tasks. These agents themselves can be powerful systems built using extensive resources and privately held data. In…Is GPT-3 Text Indistinguishable from Human Text? SCARECROW: A Framework for Scrutinizing Machine Text
Yao Dou, Maxwell Forbes, Rik Koncel-Kedziorski, Noah A. Smith, Yejin ChoiACL • 2022 Modern neural text generation systems can produce remarkably fluent and grammatical texts. While earlier language models suffered from repetition and syntactic errors, the errors made by contemporary models are often semantic, narrative, or discourse failures…Large Scale Substitution-based Word Sense Induction
Matan Eyal, Shoval Sadde, Hillel Taub-Tabib, Yoav GoldbergACL • 2022 We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus which is sense-tagged according to a corpus-derived sense inventory and where…NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks
Swaroop Mishra, Arindam Mitra, Neeraj Varshney, Bhavdeep Singh Sachdeva, Peter Clark, Chitta Baral, A. KalyanACL • 2022 Given the ubiquitous nature of numbers in text, reasoning with numbers to perform simple calculations is an important skill of AI systems. While many datasets and models have been developed to this end, state-of-the-art AI systems are brittle; failing to…Productive Performance Engineering for Weather and Climate Modeling with Python
Tal Ben-Nun, Linus Groner, Florian Deconinck, Tobias Wicky, Eddie Davis, Johann P. S. Dahm, Oliver D. Elbert, Rhea George, Jeremy McGibbon, Lukas Trümper, Elynn Wu, Oliver Fuhrer, Thomas Schulthess, Torsten HoeflerarXiv • 2022 Earth system models are developed with a tight coupling to target hardware, often containing highly-specialized code predicated on processor characteristics. This coupling stems from using imperative languages that hard-code computation schedules and layout…Reframing Instructional Prompts to GPTk's Language
Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, Hanna HajishirziFindings of ACL • 2022 How can model designers turn task instructions into effective prompts for language models? Backed by extensive empirical analysis on GPT3, we observe important features for successful instructional prompts, and propose several reframing techniques for model…