Making Pre-Trained Language Models Better Few-Shot Learners

Making Pre-Trained Language Models Better Few-Shot Learners. Tianyu gaoyadam fischzdanqi chen yprinceton universityzmassachusetts institute of technology. Web prevailing methods for mapping large generative language models to supervised tasks may fail to sufficiently probe models’ novel capabilities.

(PDF) Selective Annotation Makes Language Models Better FewShot Learners from www.researchgate.net

Web prevailing methods for mapping large generative language models to supervised tasks may fail to sufficiently probe models’ novel capabilities. Tianyu gaoyadam fischzdanqi chen yprinceton universityzmassachusetts institute of technology.

Source: www.researchgate.net

Web prevailing methods for mapping large generative language models to supervised tasks may fail to sufficiently probe models’ novel capabilities. Tianyu gaoyadam fischzdanqi chen yprinceton universityzmassachusetts institute of technology.

Source: deepai.org

Tianyu gaoyadam fischzdanqi chen yprinceton universityzmassachusetts institute of technology. Web prevailing methods for mapping large generative language models to supervised tasks may fail to sufficiently probe models’ novel capabilities.

Source: paperswithcode.com

Web prevailing methods for mapping large generative language models to supervised tasks may fail to sufficiently probe models’ novel capabilities. Tianyu gaoyadam fischzdanqi chen yprinceton universityzmassachusetts institute of technology.

Tianyu Gaoyadam Fischzdanqi Chen Yprinceton Universityzmassachusetts Institute Of Technology.

Web prevailing methods for mapping large generative language models to supervised tasks may fail to sufficiently probe models’ novel capabilities.

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