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Internet-augmented language models through few-shot prompting for open-domain question answering
[article]
2022
arXiv
pre-print
In this work, we aim to capitalize on the unique few-shot capabilities of large-scale language models (LSLMs) to overcome some of their challenges with respect to grounding to factual and up-to-date information. Motivated by semi-parametric language models (LMs), which ground their decisions in external retrieved evidence, we use few-shot prompting to learn to condition LMs on information returned from the web using Google Search, a broad and constantly updated knowledge source. Our approach
arXiv:2203.05115v2
fatcat:ghtmd46h3rhvzn344l5qbdgpge