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+---
+date: '2022-11-23T20:50:17'
+hypothesis-meta:
+  created: '2022-11-23T20:50:17.668925+00:00'
+  document:
+    title:
+    - 2022.naacl-main.167.pdf
+  flagged: false
+  group: __world__
+  hidden: false
+  id: b_EbpGtwEe2m8tfhSKM2EQ
+  links:
+    html: https://hypothes.is/a/b_EbpGtwEe2m8tfhSKM2EQ
+    incontext: https://hyp.is/b_EbpGtwEe2m8tfhSKM2EQ/aclanthology.org/2022.naacl-main.167.pdf
+    json: https://hypothes.is/api/annotations/b_EbpGtwEe2m8tfhSKM2EQ
+  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: 2221
+      start: 1677
+      type: TextPositionSelector
+    - exact: "Suppose a human is given two sentences: \u201CNoweapons of mass destruction\
+        \ found in Iraq yet.\u201Dand \u201CWeapons of mass destruction found in Iraq.\u201D\
+        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"
+      prefix: task instructions.1 Introduction
+      suffix: "\u2217Unabridged version available on"
+      type: TextQuoteSelector
+    source: https://aclanthology.org/2022.naacl-main.167.pdf
+  text: This is a really excellent illustration of the difference in paradigm between
+    "normal" text model fine tuning and prompt-based modelling
+  updated: '2022-11-23T20:50:17.668925+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/2022/11/23/1669236617
+
+---
+
+
+
+ <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