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Keyword: autoregressive language models ×Clear all

3 claims from 1 paper

1. Computer Science › Topic Modeling

   Language Models are Few-Shot Learners

   B., Mann, Ryder et al. · arXiv (Cornell University) · 2020

   The authors trained GPT-3, a 175-billion-parameter language model, and tested it on many language tasks using only a few examples given as text, with no fine-tuning.

   Unchecked3 claims
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   1. UncheckedMaking language models much larger greatly improves their few-shot performance on varied tasks, sometimes matching earlier fine-tuned systems.“Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches.”
   2. UncheckedGPT-3, a very large language model, performs strongly on many language benchmarks, including translation, question answering and some reasoning-style tasks.“GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.”
   3. Unchecked“Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans.”