Add 'brainsteam/content/annotations/2023/01/29/1674994106.md'

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+---
+date: '2023-01-29T12:08:26'
+hypothesis-meta:
+  created: '2023-01-29T12:08:26.920806+00:00'
+  document:
+    title:
+    - 2301.11305.pdf
+  flagged: false
+  group: __world__
+  hidden: false
+  id: ovUwTp_NEe2lC8uCWsE7eg
+  links:
+    html: https://hypothes.is/a/ovUwTp_NEe2lC8uCWsE7eg
+    incontext: https://hyp.is/ovUwTp_NEe2lC8uCWsE7eg/arxiv.org/pdf/2301.11305.pdf
+    json: https://hypothes.is/api/annotations/ovUwTp_NEe2lC8uCWsE7eg
+  permissions:
+    admin:
+    - acct:ravenscroftj@hypothes.is
+    delete:
+    - acct:ravenscroftj@hypothes.is
+    read:
+    - group:__world__
+    update:
+    - acct:ravenscroftj@hypothes.is
+  tags:
+  - chatgpt
+  - detecting gpt
+  target:
+  - selector:
+    - end: 16098
+      start: 15348
+      type: TextPositionSelector
+    - exact: "Figure 3. The average drop in log probability (perturbation discrep-ancy)\
+        \ after rephrasing a passage is consistently higher for model-generated passages\
+        \ than for human-written passages. Each plotshows the distribution of the\
+        \ perturbation discrepancy d (x, p\u03B8 , q)for human-written news articles\
+        \ and machine-generated arti-cles; of equal word length from models GPT-2\
+        \ (1.5B), GPT-Neo-2.7B (Black et al., 2021), GPT-J (6B; Wang & Komatsuzaki\
+        \ (2021))and GPT-NeoX (20B; Black et al. (2022)). Human-written arti-cles\
+        \ are a sample of 500 XSum articles; machine-generated textis generated by\
+        \ prompting each model with the first 30 tokens ofeach XSum article, sampling\
+        \ from the raw conditional distribution.Discrepancies are estimated with 100\
+        \ T5-3B samples."
+      prefix: ancy)0.00.20.40.60.81.0Frequency
+      suffix: to machine-generated text detect
+      type: TextQuoteSelector
+    source: https://arxiv.org/pdf/2301.11305.pdf
+  text: quite striking here is the fact that more powerful/larger models are more
+    capable of generating unusual or "human-like" responses - looking at the overlap
+    in log likelihoods
+  updated: '2023-01-29T12:08:26.920806+00:00'
+  uri: https://arxiv.org/pdf/2301.11305.pdf
+  user: acct:ravenscroftj@hypothes.is
+  user_info:
+    display_name: James Ravenscroft
+in-reply-to: https://arxiv.org/pdf/2301.11305.pdf
+tags:
+- chatgpt
+- detecting gpt
+- hypothesis
+type: annotation
+url: /annotations/2023/01/29/1674994106
+
+---
+
+
+
+ <blockquote>Figure 3. The average drop in log probability (perturbation discrep-ancy) after rephrasing a passage is consistently higher for model-generated passages than for human-written passages. Each plotshows the distribution of the perturbation discrepancy d (x, pθ , q)for human-written news articles and machine-generated arti-cles; of equal word length from models GPT-2 (1.5B), GPT-Neo-2.7B (Black et al., 2021), GPT-J (6B; Wang & Komatsuzaki (2021))and GPT-NeoX (20B; Black et al. (2022)). Human-written arti-cles are a sample of 500 XSum articles; machine-generated textis generated by prompting each model with the first 30 tokens ofeach XSum article, sampling from the raw conditional distribution.Discrepancies are estimated with 100 T5-3B samples.</blockquote>quite striking here is the fact that more powerful/larger models are more capable of generating unusual or "human-like" responses - looking at the overlap in log likelihoods