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+---
+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
\ No newline at end of file