A Review of Winograd Schema Challenge Datasets and Approaches

A Review of Winograd Schema Challenge Datasets and Approaches

23 April 2020

Thomas Lukasiewicz

Papers citing "A Review of Winograd Schema Challenge Datasets and Approaches"

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Title
Saliency-driven Dynamic Token Pruning for Large Language Models Yao Tao Yehui Tang Yun Wang Mingjian Zhu Hailin Hu Yunhe Wang 34 0 0 06 Apr 2025
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization Zhe Li Bicheng Ying Zidong Liu Haibo Yang Haibo Yang FedML 59 3 0 24 May 2024
Experience and Prediction: A Metric of Hardness for a Novel Litmus Test Nicos Isaak Loizos Michael 24 3 0 05 Sep 2023
Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models Hao Liu Xinyang Geng Lisa Lee Igor Mordatch Sergey Levine Sharan Narang Pieter Abbeel KELM CLL 33 2 0 24 Oct 2022
Evaluate Confidence Instead of Perplexity for Zero-shot Commonsense Reasoning Letian Peng Z. Li Hai Zhao ReLM LRM 16 1 0 23 Aug 2022
Meaning without reference in large language models S. Piantadosi Felix Hill 14 72 0 05 Aug 2022
Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models T. Klein Moin Nabi ReLM LRM 27 8 0 10 Sep 2021
Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema Yanai Elazar Hongming Zhang Yoav Goldberg Dan Roth ReLM LRM 32 44 0 16 Apr 2021
Precise Task Formalization Matters in Winograd Schema Evaluations Haokun Liu William Huang Dhara Mungra Samuel R. Bowman ReLM 9 12 0 08 Oct 2020
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 294 6,950 0 20 Apr 2018