61 lines
2.3 KiB
Markdown
61 lines
2.3 KiB
Markdown
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---
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date: '2022-11-23T20:52:10'
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hypothesis-meta:
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created: '2022-11-23T20:52:10.292273+00:00'
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document:
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title:
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- 2022.naacl-main.167.pdf
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flagged: false
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group: __world__
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hidden: false
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id: sxEWFGtwEe2_zFc3H2nb2Q
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links:
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html: https://hypothes.is/a/sxEWFGtwEe2_zFc3H2nb2Q
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incontext: https://hyp.is/sxEWFGtwEe2_zFc3H2nb2Q/aclanthology.org/2022.naacl-main.167.pdf
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json: https://hypothes.is/api/annotations/sxEWFGtwEe2_zFc3H2nb2Q
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permissions:
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admin:
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- acct:ravenscroftj@hypothes.is
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delete:
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- acct:ravenscroftj@hypothes.is
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read:
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- group:__world__
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update:
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- acct:ravenscroftj@hypothes.is
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tags:
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- prompt-models
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- NLProc
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target:
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- selector:
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- end: 1663
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start: 1398
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type: TextPositionSelector
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- exact: "Insum, notwithstanding prompt-based models\u2019impressive improvement,\
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\ we find evidence ofserious limitations that question the degree towhich\
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\ such improvement is derived from mod-els understanding task instructions\
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\ in waysanalogous to humans\u2019 use of task instructions."
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prefix: 'ing prompts even at zero shots. '
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suffix: 1 IntroductionSuppose a human is
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type: TextQuoteSelector
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source: https://aclanthology.org/2022.naacl-main.167.pdf
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text: although prompts seem to help NLP models improve their performance, the authors
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find that this performance is still present even when prompts are deliberately
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misleading which is a bit weird
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updated: '2022-11-23T20:52:10.292273+00:00'
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uri: https://aclanthology.org/2022.naacl-main.167.pdf
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user: acct:ravenscroftj@hypothes.is
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user_info:
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display_name: James Ravenscroft
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in-reply-to: https://aclanthology.org/2022.naacl-main.167.pdf
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tags:
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- prompt-models
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- NLProc
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- hypothesis
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type: reply
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url: /replies/2022/11/23/1669236730
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---
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<blockquote>Insum, notwithstanding prompt-based models’impressive improvement, we find evidence ofserious limitations that question the degree towhich such improvement is derived from mod-els understanding task instructions in waysanalogous to humans’ use of task instructions.</blockquote>although prompts seem to help NLP models improve their performance, the authors find that this performance is still present even when prompts are deliberately misleading which is a bit weird
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