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Rodney Michael Kinney, Chloe Anastasiades, Russell Authur, Iz Beltagy, Jonathan Bragg, Alexandra Buraczynski, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Arman Cohan, Miles Crawford, Doug Downey, J. Dunkelberger, Oren Etzioni, R. Evans, Sergey Feldman, Joseph Gorney, D. Graham, F.Q. Hu, Regan Huff, Daniel King, Sebastian Kohlmeier, Bailey Kuehl, Michael Langan, Daniel Lin, Haokun Liu, Kyle Lo, Jaron Lochner, Kelsey MacMillan, Tyler Murray, Christopher Newell, Smita Rao, Shaurya Rohatgi, P. Sayre, Zejiang Shen, Amanpreet Singh, Luca Soldaini, Shivashankar Subramanian, A. Tanaka, Alex D Wade, Linda M. Wagner, Lucy Lu Wang, Christopher Wilhelm, Caroline Wu, Jiangjiang Yang, A. Zamarron, Madeleine van Zuylen, Daniel S. WeldarXiv • 2023 The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field. Semantic Scholar (S2) is an open data platform and website aimed at accelerating science by helping scholars discover…Does progress on ImageNet transfer to real-world datasets?
Alexander W. Fang, Simon Kornblith, Ludwig SchmidtarXiv • 2023 Does progress on ImageNet transfer to real-world datasets? We investigate this question by evaluating ImageNet pre-trained models with varying accuracy (57% - 83%) on six practical image classification datasets. In particular, we study datasets collected with…MAUVE Scores for Generative Models: Theory and Practice
Krishna Pillutla, Lang Liu, John Thickstun, S. Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Z. HarchaouiarXiv • 2022 Generative AI has matured to a point where large-scale models can generate text that seems indistinguishable from human-written text and remarkably photorealistic images. Automatically measuring how close the distribution of generated data is to the target…Do language models have coherent mental models of everyday things?
Yuling Gu, Bhavana Dalvi Mishra, Peter ClarkarXiv • 2022 When people think of everyday things like an “egg,” they typically have a mental image associated with it. This commonsense knowledge helps us understand how these everyday things work and how to interact with them. For example, when someone tries to make a…Detoxifying Text with MaRCo: Controllable Revision with Experts and Anti-Experts
Skyler Hallinan, Alisa Liu, Yejin Choi, Maarten SaparXiv • 2022 Text detoxification has the potential to miti- 001 gate the harms of toxicity by rephrasing text to 002 remove offensive meaning, but subtle toxicity 003 remains challenging to tackle. We introduce 004 M A RC O , a detoxification algorithm that com- 005 bines…DISCO: Distilling Phrasal Counterfactuals with Large Language Models
Zeming Chen, Qiyue Gao, Kyle Richardson, Antoine Bosselut, Ashish SabharwalarXiv • 2022 Recent methods demonstrate that data augmentation using counterfactual knowledge can teach models the causal structure of a task, leading to robust and generalizable models. However, such counterfactual data often has a limited scale and diversity if…Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, Ashish SabharwalarXiv • 2022 Recent work has shown that large language models are capable of generating natural language reasoning steps or Chains-of-Thoughts (CoT) to answer a multi-step question when prompted to do so. This is insufficient, however, when the necessary knowledge is not…Reinforced Clarification Question Generation with Defeasibility Rewards for Disambiguating Social and Moral Situations
Valentina Pyatkin, Jena D. Hwang, Vivek Srikumar, Ximing Lu, Liwei Jiang, Yejin Choi, Chandra BhagavatulaarXiv • 2022 Context is vital for commonsense moral reasoning. “Lying to a friend" is wrong if it is meant to deceive them, but may be morally okay if it is intended to protect them. Such nuanced but salient contextual information can potentially flip the moral judgment of…Reinforced Clarification Question Generation with Defeasibility Rewards for Disambiguating Social and Moral Situations
Valentina Pyatkin, Jena D. Hwang, Vivek Srikumar, Ximing Lu, Liwei Jiang, Yejin Choi, Chandra BhagavatulaarXiv • 2022 Context is vital for commonsense moral reasoning. “Lying to a friend" is wrong if it is meant to deceive them, but may be morally okay if it is intended to protect them. Such nuanced but salient contextual information can potentially flip the moral judgment of…SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization
Hyunwoo Kim, Jack Hessel, Liwei Jiang, Ximing Lu, Youngjae Yu, Pei Zhou, Ronan Le Bras, Malihe Alikhani, Gunhee Kim, Maarten Sap, Yejin ChoiarXiv • 2022 We present S ODA : the first publicly available, million-scale high-quality social dialogue dataset. Using S ODA , we train C OSMO : a generalizable conversation agent outperforming previous best-performing agents on both in- and out-of-domain datasets. In…