Papers Winowhy
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PaLM: Scaling Language Modeling with Pathways
Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed t…
Auto DebuggingCode GenerationCommon Sense ReasoningCoreference Resolution+19Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence …
AnachronismsAnalogical SimilarityAnalytic EntailmentCausal Judgment+69Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis o…
Abstract AlgebraAnachronismsAnalogical SimilarityAnalytic Entailment+143Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference
Recent methods based on pre-trained language models have shown strong supervised performance on commonsense reasoning. However, they rely on expensive data annotation and time-consuming training. Thus, we focus on unsupe…
Natural Language InferenceTransfer LearningWinowhyWinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challenge
In this paper, we present the first comprehensive categorization of essential commonsense knowledge for answering the Winograd Schema Challenge (WSC). For each of the questions, we invite annotators to first provide reas…
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