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A taxonomy and review of generalization research in NLP
D. Hupkes, Mario Giulianelli, Verna Dankers, Mikel Artetxe, Yanai Elazar, Tiago Pimentel, Christos Christodoulopoulos, Karim Lasri, Naomi Saphra, Arabella J. Sinclair, Dennis Ulmer, Florian Schottmann, Khuyagbaatar Batsuren, Kaiser Sun, Koustuv Sinha, Leila Khalatbari, Maria Ryskina, Rita Frieske, Ryan Cotterell, Zhijing JinNature Machine Intelligence • 2023 The ability to generalise well is one of the primary desiderata of natural language processing (NLP). Yet, what ‘good generalisation’ entails and how it should be evaluated is not well understood, nor are there any evaluation standards for generalisation. In…The Expressive Power of Transformers with Chain of Thought
William Merrill, Ashish SabharwalarXiv • 2023 Recent theoretical work has identified surprisingly simple reasoning problems, such as checking if two nodes in a graph are connected or simulating finite-state machines, that are provably unsolvable by standard transformers that answer immediately after…TRAM: Bridging Trust Regions and Sharpness Aware Minimization
Tom Sherborne, Naomi Saphra, Pradeep Dasigi, Hao PengarXiv • 2023 By reducing the curvature of the loss surface in the parameter space, Sharpness-aware minimization (SAM) yields widespread robustness improvement under domain transfer. Instead of focusing on parameters, however, this work considers the transferability of…ACE: A fast, skillful learned global atmospheric model for climate prediction
Oliver Watt‐Meyer, Gideon Dresdner, J. McGibbon, Spencer K. Clark, Brian Henn, James Duncan, Noah Brenowitz, K. Kashinath, Michael S. Pritchard, B. Bonev, Matthew E. Peters, Christopher S. BrethertonarXiv • 2023 Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existing…Closing the Curious Case of Neural Text Degeneration
Matthew Finlayson, John Hewitt, Alexander Koller, Swabha Swayamdipta, Ashish SabharwalarXiv • 2023 Despite their ubiquity in language generation, it remains unknown why truncation sampling heuristics like nucleus sampling are so effective. We provide a theoretical explanation for the effectiveness of the truncation sampling by proving that truncation…Making Retrieval-Augmented Language Models Robust to Irrelevant Context
Ori Yoran, Tomer Wolfson, Ori Ram, Jonathan BerantarXiv.org • 2023 Retrieval-augmented language models (RALMs) hold promise to produce language understanding systems that are are factual, efficient, and up-to-date. An important desideratum of RALMs, is that retrieved information helps model performance when it is relevant…SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding
Favyen Bastani, Piper Wolters, Ritwik Gupta, Joe Ferdinando, Aniruddha KembhaviICCV • 2023 Remote sensing images are useful for a wide variety of planet monitoring applications, from tracking deforestation to tackling illegal fishing. The Earth is extremely diverse -- the amount of potential tasks in remote sensing images is massive, and the sizes…The Surveillance AI Pipeline
Pratyusha Ria Kalluri, William Agnew, M. Cheng, Kentrell Owens, Luca Soldaini, A. BirhanearXiv • 2023 A rapidly growing number of voices have argued that AI research, and computer vision in particular, is closely tied to mass surveillance. Yet the direct path from computer vision research to surveillance has remained obscured and difficult to assess. This…When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets
Orion Weller, Kyle Lo, David Wadden, Dawn J Lawrie, Benjamin Van Durme, Arman Cohan, Luca SoldainiarXiv • 2023 Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. However, it is unknown whether these techniques are universally beneficial or only effective in specific settings, such as for particular…A machine learning parameterization of clouds in a coarse-resolution climate model for unbiased radiation
Brian Henn, Y. R. Jauregui, Spencer K. Clark, Noah Brenowitz, J. McGibbon, Oliver Watt‐Meyer, Andrew G. Pauling, C. BrethertonESSOAr • 2023 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…