64 lines
2.8 KiB
Markdown
64 lines
2.8 KiB
Markdown
---
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date: '2022-11-23T20:50:17'
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hypothesis-meta:
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created: '2022-11-23T20:50:17.668925+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: b_EbpGtwEe2m8tfhSKM2EQ
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links:
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html: https://hypothes.is/a/b_EbpGtwEe2m8tfhSKM2EQ
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incontext: https://hyp.is/b_EbpGtwEe2m8tfhSKM2EQ/aclanthology.org/2022.naacl-main.167.pdf
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json: https://hypothes.is/api/annotations/b_EbpGtwEe2m8tfhSKM2EQ
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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: 2221
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start: 1677
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type: TextPositionSelector
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- exact: "Suppose a human is given two sentences: \u201CNoweapons of mass destruction\
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\ found in Iraq yet.\u201Dand \u201CWeapons of mass destruction found in Iraq.\u201D\
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They are then asked to respond 0 or 1 and receive areward if they are correct.\
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\ In this setup, they wouldlikely need a large number of trials and errors\
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\ be-fore figuring out what they are really being re-warded to do. This setup\
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\ is akin to the pretrain-and-fine-tune setup which has dominated NLP in recentyears,\
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\ in which models are asked to classify a sen-tence representation (e.g.,\
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\ a CLS token) into some"
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prefix: task instructions.1 Introduction
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suffix: "\u2217Unabridged version available on"
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type: TextQuoteSelector
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source: https://aclanthology.org/2022.naacl-main.167.pdf
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text: This is a really excellent illustration of the difference in paradigm between
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"normal" text model fine tuning and prompt-based modelling
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updated: '2022-11-23T20:50:17.668925+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: annotation
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url: /annotation/2022/11/23/1669236617
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---
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<blockquote>Suppose a human is given two sentences: “Noweapons of mass destruction found in Iraq yet.”and “Weapons of mass destruction found in Iraq.”They are then asked to respond 0 or 1 and receive areward if they are correct. In this setup, they wouldlikely need a large number of trials and errors be-fore figuring out what they are really being re-warded to do. This setup is akin to the pretrain-and-fine-tune setup which has dominated NLP in recentyears, in which models are asked to classify a sen-tence representation (e.g., a CLS token) into some</blockquote>This is a really excellent illustration of the difference in paradigm between "normal" text model fine tuning and prompt-based modelling |