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Found 4 papers, 2 papers with code
OPT: Open Pre-trained Transformer Language Models
Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning.
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Few-shot Learning with Multilingual Language Models
In this work, we train multilingual autoregressive language models on a balanced corpus covering a diverse set of languages, and study their few- and zero-shot learning capabilities in a wide range of tasks.
Efficient Large Scale Language Modeling with Mixtures of Experts
Mixture of Experts layers (MoEs) enable efficient scaling of language models through conditional computation.
MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark
Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets.
