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1,140 claims from 718 papers are on the record. 39 have been checked so far; the other 1,101 have no check with a result yet.

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Keyword: few-shot learning ×Clear all

6 claims from 3 papers

1. Computer Science › Topic Modeling

   PaLM: Scaling Language Modeling with Pathways

   Chowdhery, Narang, Devlin et al. · arXiv (Cornell University) · 2022

   The authors trained PaLM, a 540-billion parameter language model, and report state-of-the-art few-shot results on hundreds of benchmarks, plus analyses of bias, toxicity and memorisation.

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   1. UncheckedMany BIG-bench tasks showed sudden, steep gains in performance when the model reached the largest size the authors trained, PaLM 540B.“A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model.”
   2. UncheckedThe authors report that scaling a language model up to 540 billion parameters gave state-of-the-art few-shot results on hundreds of benchmarks.“We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks.”
   3. Unchecked“On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark.”

2. Computer Science › Domain Adaptation and Few-Shot Learning

   The Power of Scale for Parameter-Efficient Prompt Tuning

   Lester, Al‐Rfou and Constant · Conference on Empirical Methods in Natural Language Processing (EMNLP) · 2021

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   1. Unchecked“Our end-to-end learned approach outperforms GPT-3's "few-shot" learning by a large margin.”
   2. Unchecked“More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method "closes the gap" and matches the strong performance of model tuning (where all mode…

3. Computer Science › Topic Modeling

   Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

   Smith, Patwary, Norick et al. · arXiv (Cornell University) · 2022

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   1. Unchecked“We demonstrate that MT-NLG achieves superior zero-, one-, and few-shot learning accuracies on several NLP benchmarks and establishes new state-of-the-art results.”