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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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Stereotypical Bias Analysis
on CrowS-Pairs
Efficient Large Scale Language Modeling with Mixtures of Experts
This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings: in- and out-of-domain language modeling, zero- and few-shot priming, and full-shot fine-tuning.
Few-shot Learning with Multilingual Language Models
Large-scale generative language models such as GPT-3 are competitive few-shot learners.
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.
