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Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Training Compute-Optimal Large Language Models
TIMEDIAL: Temporal Commonsense Reasoning in Dialog
Papers
Training 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+69An Application of Pseudo-Log-Likelihoods to Natural Language Scoring
Language models built using semi-supervised machine learning on large corpora of natural language have very quickly enveloped the fields of natural language generation and understanding. In this paper we apply a zero-sho…
Common Sense ReasoningGPULanguage ModelingLanguage Modelling+3Scaling 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+143TIMEDIAL: Temporal Commonsense Reasoning in Dialog
Everyday conversations require understanding everyday events, which in turn, requires understanding temporal commonsense concepts interwoven with those events. Despite recent progress with massive pre-trained language mo…
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