Language models are few shot learners

  • Learning a Deep Embedding Model for Zero-Shot Learning (DEM)[8] Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs (GCN) [10] Learning to Compare: Relation Network for Few-Shot Learning (RN)[11] 论文都是在解决一个问题,那就是如何求特征空间到语义空间的映射矩阵W的问题。 1、Zero-shot ...
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GitHub - openai/gpt-3: GPT-3: Language Models are Few-Shot Learners. arXiv link Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre... 概要を表示 arXiv link Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on ...

Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh Nguyen, Matthias Hein and Bernt Schiele. We present a novel latent embedding model for learning a compatibility function between image and class embeddings, in the context of zero-shot classification.
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  • The few-shot setting instead allows us to "frame" the task as a cloze-test and allows the language model to infer from examples that a completion of exactly one word is desired. GPT-3 is shown in the few-, one-, and zero-shot settings, as compared to prior SOTA results for closed book and open domain settings.
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    As indicated by the name, few-shot learning as described here for language models is related to few-shot learning as used in other contexts in ML [HYC01, VBL+16] – both involve learning based on a broad distribution of tasks (in this case implicit in the pre-training data) and then rapidly adapting to a new task.

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    approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via ...

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    As indicated by the name, few-shot learning as described here for language models is related to few-shot learning as used in other contexts in ML [HYC01, VBL + 16] – both involve learning based on a broad distribution of tasks (in this case implicit in the pre-training data) and then rapidly adapting to a new task.

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    Nov 23, 2020 · A trustworthy model can have squares that are off the diagonal, but those squares should be colored brightly as well. A bad model will quickly show itself with dark-colored squares. For instance, the red circle represents a “switch” that was predicted as a “street sign” by the machine learning model with a low trust score.

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    Few Shot One Shot Zero Shot 0.4B 0.8B 1.3B 2.6B 6.7B 13B Parameters in LM (Billions) 175B Zero-shot 60 50 40 30 20 10 One-shot Natural Language Prompt No Prompt 10 Few-shot 175B Params 13B Params 1.3B Params 10 Number of Examples in Context (K)

    Extensively Matching for Few-shot Learning Event Detection Viet Lai, Franck Dernoncourt and Thien Huu Nguyen Proceedings of the 1st Joint Workshop on Narrative Understanding, Storylines, and Events (NUSE) at ACL 2020, Seattle, USA, July 2020 Exploiting the Syntax-Model Consistency for Neural Relation Extraction

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