Papers
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Viewing 41-50 of 1021 papers
A machine learning parameterization of clouds in a coarse-resolution climate model for unbiased radiation
Brian Henn, Yakelyn R. Jauregui, Spencer K. Clark, Noah Brenowitz, Jeremy McGibbon, Oliver Watt‐Meyer, Andrew G. Pauling, Christopher S. BrethertonJAMES • 2024 Coarse-grid weather and climate models rely particularly on parameterizations of cloud fields, and coarse-grained cloud fields from a fine-grid reference model are a natural target for a machine-learned parameterization. We machine-learn the coarsened-fine…Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic
Nathaniel Weir, Kate Sanders, Orion Weller, Shreya Sharma, Dongwei Jiang, Zhengping Jiang, Bhavana Dalvi, Oyvind Tafjord, Peter Alexander Jansen, Peter Clark, Benjamin Van DurmearXiv.org • 2024 Contemporary language models enable new opportunities for structured reasoning with text, such as the construction and evaluation of intuitive, proof-like textual entailment trees without relying on brittle formal logic. However, progress in this direction…A Survey on Data Selection for Language Models
Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre, Nathan Lambert, Xinyi Wang, Niklas Muennighoff, Bairu Hou, Liangming Pan, Haewon Jeong, Colin Raffel, Shiyu Chang, Tatsunori Hashimoto, William Yang WangarXiv • 2024 A major factor in the recent success of large language models is the use of enormous and ever-growing text datasets for unsupervised pre-training. However, naively training a model on all available data may not be optimal (or feasible), as the quality of…Application of the AI2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity
James P. C. Duncan, Elynn Wu, Jean-Christoph Golaz, Peter M. Caldwell, Oliver Watt-Meyer, Spencer K. Clark, Jeremy McGibbon, Gideon Dresdner, Karthik Kashinath, Boris Bonev, Michael S. Pritchard, and Christopher S. BrethertonAuthorea • 2024 Can the current successes of global machine learning-based weather simulators be generalized beyond two-week forecasts to stable and accurate multiyear runs? The recently developed AI2 Climate Emulator (ACE) suggests this is feasible, based upon 10-year…Calibrating Large Language Models with Sample Consistency
Qing Lyu, Kumar Shridhar, Chaitanya Malaviya, Li Zhang, Yanai Elazar, Niket Tandon, Marianna Apidianaki, Mrinmaya Sachan, Chris Callison-BurcharXiv • 2024 Accurately gauging the confidence level of Large Language Models' (LLMs) predictions is pivotal for their reliable application. However, LLMs are often uncalibrated inherently and elude conventional calibration techniques due to their proprietary nature and…Improving Stratocumulus Cloud Amounts in a 200‐m Resolution Multi‐Scale Modeling Framework Through Tuning of Its Interior Physics
Liran Peng, P. Blossey, W. Hannah, C. Bretherton, C. Terai, A. Jenney, M. PritchardJournal of Advances in Modeling Earth Systems • 2024 High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due…Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties
Taylor Sorensen, Liwei Jiang, Jena D. Hwang, Sydney Levine, Valentina Pyatkin, Peter West, Nouha Dziri, Ximing Lu, Kavel Rao, Chandra Bhagavatula, Maarten Sap, J. Tasioulas, Yejin ChoiAAAI • 2024 Human values are crucial to human decision-making. Value pluralism is the view that multiple correct values may be held in tension with one another (e.g., when considering lying to a friend to protect their feelings, how does one balance honesty with…Global Precipitation Correction Across a Range of Climates Using CycleGAN
Jeremy McGibbon, S. K. Clark, Brian Henn, Anna Kwa, Oliver Watt‐Meyer, W. Perkins, Christopher S. Bretherton, S. K. ClarkGeophysical Research Letters • 2024 Accurate precipitation simulations for various climate scenarios are critical for understanding and predicting the impacts of climate change. This study employs a Cycle‐generative adversarial network (CycleGAN) to improve global 3‐hr‐average precipitation…TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation
Yikai Zhang, Siyu Yuan, Caiyu Hu, Kyle Richardson, Yanghua Xiao, Jiangjie ChenarXiv • 2024 Despite remarkable advancements in emulating human-like behavior through Large Language Models (LLMs), current textual simulations do not adequately address the notion of time. To this end, we introduce TimeArena, a novel textual simulated environment that…OLMo: Accelerating the Science of Language Models
Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, A. Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Daniel Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, Hanna HajishirziarXiv • 2024 Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of…