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1,143 claims from 718 papers are on the record. 41 have been checked so far; the other 1,102 have no check with a result yet.

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Status: Unchecked ×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 scaling, 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. UncheckedOn some tasks, the 540-billion-parameter PaLM model beat the finetuned state of the art on multi-step reasoning and average human performance on BIG-bench.“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

   The paper introduces prompt tuning, which learns soft prompts for frozen language models, and reports it matches full model tuning at large scale and aids robustness to domain transfer.

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   1. UncheckedThe paper says its prompt tuning method, which learns soft prompts for a frozen language model, beats GPT-3's few-shot learning by a large margin.“Our end-to-end learned approach outperforms GPT-3's "few-shot" learning by a large margin.”
   2. UncheckedIn T5 experiments, prompt tuning matches full model tuning once models pass billions of parameters, though it lags behind at smaller sizes.“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 model weights are tuned).”

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.”