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f:{"text":"Risk identification, assessment and mitigation","id":"risk-identification-assessment-and-mitigation","headlineTag":"h2","omitFromTableOfContents":false}
10:{"text":"External Red Teaming","id":"external-red-teaming","headlineTag":"h2","omitFromTableOfContents":false}
11:{"text":"Evaluation methodology","id":"evaluation-methodology","headlineTag":"h2","omitFromTableOfContents":false}
12:{"text":"Limitations of the evaluation methodology","id":"limitations-of-the-evaluation-methodology","headlineTag":"h3","omitFromTableOfContents":true}
13:{"text":"Observed safety challenges, evaluations & mitigations","id":"observed-safety-challenges-evaluations-and-mitigations","headlineTag":"h2","omitFromTableOfContents":false}
14:{"text":"Unauthorized voice generation","id":"unauthorized-voice-generation","headlineTag":"h3","omitFromTableOfContents":true}
15:{"text":"Speaker identification","id":"speaker-identification","headlineTag":"h3","omitFromTableOfContents":true}
16:{"text":"Disparate performance on voice inputs","id":"disparate-performance-on-voice-inputs","headlineTag":"h3","omitFromTableOfContents":true}
17:{"text":"Ungrounded Inference / Sensitive Trait Attribution","id":"ungrounded-inference-sensitive-trait-attribution","headlineTag":"h3","omitFromTableOfContents":true}
18:{"text":"Violative & disallowed content","id":"violative-and-disallowed-content","headlineTag":"h3","omitFromTableOfContents":true}
19:{"text":"Erotic & violent speech content","id":"erotic-and-violent-speech-content","headlineTag":"h3","omitFromTableOfContents":true}
1a:{"text":"Other known risks and limitations of the model","id":"other-known-risks-and-limitations-of-the-model","headlineTag":"h3","omitFromTableOfContents":true}
1b:{"text":"Preparedness framework evaluations","id":"preparedness-framework-evaluations","headlineTag":"h2","omitFromTableOfContents":false}
1c:{"text":"Preparedness framework","id":"preparedness-framework","headlineTag":"h3","omitFromTableOfContents":true}
1d:{"text":"Cybersecurity","id":"cybersecurity","headlineTag":"h3","omitFromTableOfContents":true}
1e:{"text":"Biological threats","id":"biological-threats","headlineTag":"h3","omitFromTableOfContents":true}
1f:{"text":"Persuasion","id":"persuasion","headlineTag":"h3","omitFromTableOfContents":true}
20:{"text":"Model autonomy","id":"model-autonomy","headlineTag":"h3","omitFromTableOfContents":true}
21:{"text":"Third party assessments","id":"third-party-assessments","headlineTag":"h2","omitFromTableOfContents":false}
22:{"text":"METR assessment","id":"metr-assessment","headlineTag":"h3","omitFromTableOfContents":true}
23:{"text":"Apollo research assessment","id":"apollo-research-assessment","headlineTag":"h3","omitFromTableOfContents":true}
24:{"text":"Apollo Evaluations run on GPT-4o","id":"apollo-evaluations-run-on-gpt-4o","headlineTag":"h2","omitFromTableOfContents":true}
25:{"text":"Societal impacts","id":"societal-impacts","headlineTag":"h2","omitFromTableOfContents":false}
26:{"text":"Anthropomorphization and emotional reliance","id":"anthropomorphization-and-emotional-reliance","headlineTag":"h2","omitFromTableOfContents":true}
27:{"text":"Health","id":"health","headlineTag":"h2","omitFromTableOfContents":true}
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2a:{"text":"Underrepresented Languages","id":"underrepresented-languages","headlineTag":"h2","omitFromTableOfContents":true}
2b:{"text":"Conclusion and next steps","id":"conclusion-and-next-steps","headlineTag":"h2","omitFromTableOfContents":false}
2c:{"text":"Appendix","id":"appendix","headlineTag":"h2","omitFromTableOfContents":false}
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2e:{"text":"B - Sample tasks from METR Evaluations","id":"b-sample-tasks-from-metr-evaluations","headlineTag":"h2","omitFromTableOfContents":true}
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40:T51f,For the voice modality, we updated the study methodology to measure effect sizes on hypothetical party preferences, and the effect sizes’ persistence one week later. We evaluated the persuasiveness of GPT‑4o voiced audio clips and interactive (multi-turn) conversations relative to human baselines (listening to a static human-generated audio clip or engaging in a conversation with another human). We found that for both interactive multi-turn conversations and audio clips, the GPT‑4o voice model was not more persuasive than a human. Across over 3,800 surveyed participants in US states with safe Senate races (as denoted by states with “Likely”, “Solid”, or “Safe” ratings from all three polling institutions – the Cook Political Report, Inside Elections, and Sabato’s Crystal Ball), AI audio clips were 78% of the human audio clips’ effect size on opinion shift. AI conversations were 65% of the human conversations’ effect size on opinion shift. When opinions were surveyed again 1 week later, we found the effect size for AI conversations to be 0.8%, while for AI audio clips, the effect size was -0.72%. 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