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Status: Unchecked ×Keyword: zero-shot learning ×Clear all

5 claims from 3 papers

1. 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.”

2. Computer Science › Topic Modeling

   Crosslingual Generalization through Multitask Finetuning

   Muennighoff, Thomas, Sutawika et al. · arXiv (Cornell University) · 2022

   The authors finetune multilingual BLOOM and mT5 models on many prompted tasks, producing BLOOMZ and mT0, and study how well they generalise zero-shot across languages.

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   1. UncheckedFinetuning large multilingual models on English tasks with English prompts can transfer to non-English languages seen only in pretraining.“We find finetuning large multilingual language models on English tasks with English prompts allows for task generalization to non-English languages that appear only in the pretraining corpus.”
   2. UncheckedFinetuning multilingual models on multilingual tasks with English prompts further improves English and non-English performance, giving state-of-the-art zero-shot results.“Finetuning on multilingual tasks with English prompts further improves performance on English and non-English tasks leading to various state-of-the-art zero-shot results.”
   3. UncheckedFinetuned multilingual models can perform new tasks zero-shot in languages they were never intentionally exposed to, according to the paper.“Surprisingly, we find models are capable of zero-shot generalization to tasks in languages they have never intentionally seen.”

3. Computer Science › Topic Modeling

   Task Contamination: Language Models May Not Be Few-Shot Anymore

   Li and Flanigan · arXiv (Cornell University) · 2023

   The paper examines whether zero-shot and few-shot results of large language models are inflated by task contamination, tracking performance over time and using several methods to look for evidence of it.

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   1. UncheckedIn this study, language models did surprisingly better on datasets released before their training data was created than on later ones, after controlling for difficulty.“Utilizing GPT-3 series models and several other recent open-sourced LLMs, and controlling for dataset difficulty, we find that on datasets released before the LLM training data creation date, LLMs perform surprisingly better than on datasets released after.”