61 lines
2.3 KiB
Markdown
61 lines
2.3 KiB
Markdown
---
|
||
date: '2022-11-23T20:52:10'
|
||
hypothesis-meta:
|
||
created: '2022-11-23T20:52:10.292273+00:00'
|
||
document:
|
||
title:
|
||
- 2022.naacl-main.167.pdf
|
||
flagged: false
|
||
group: __world__
|
||
hidden: false
|
||
id: sxEWFGtwEe2_zFc3H2nb2Q
|
||
links:
|
||
html: https://hypothes.is/a/sxEWFGtwEe2_zFc3H2nb2Q
|
||
incontext: https://hyp.is/sxEWFGtwEe2_zFc3H2nb2Q/aclanthology.org/2022.naacl-main.167.pdf
|
||
json: https://hypothes.is/api/annotations/sxEWFGtwEe2_zFc3H2nb2Q
|
||
permissions:
|
||
admin:
|
||
- acct:ravenscroftj@hypothes.is
|
||
delete:
|
||
- acct:ravenscroftj@hypothes.is
|
||
read:
|
||
- group:__world__
|
||
update:
|
||
- acct:ravenscroftj@hypothes.is
|
||
tags:
|
||
- prompt-models
|
||
- NLProc
|
||
target:
|
||
- selector:
|
||
- end: 1663
|
||
start: 1398
|
||
type: TextPositionSelector
|
||
- exact: "Insum, notwithstanding prompt-based models\u2019impressive 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\u2019 use of task instructions."
|
||
prefix: 'ing prompts even at zero shots. '
|
||
suffix: 1 IntroductionSuppose a human is
|
||
type: TextQuoteSelector
|
||
source: https://aclanthology.org/2022.naacl-main.167.pdf
|
||
text: 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
|
||
updated: '2022-11-23T20:52:10.292273+00:00'
|
||
uri: https://aclanthology.org/2022.naacl-main.167.pdf
|
||
user: acct:ravenscroftj@hypothes.is
|
||
user_info:
|
||
display_name: James Ravenscroft
|
||
in-reply-to: https://aclanthology.org/2022.naacl-main.167.pdf
|
||
tags:
|
||
- prompt-models
|
||
- NLProc
|
||
- hypothesis
|
||
type: reply
|
||
url: /replies/2022/11/23/1669236730
|
||
|
||
---
|
||
|
||
|
||
|
||
<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 |