claim
active
claim:gpt-4-s-relatively-poor-performance-on-unethical-instrumental-intent-is-due-to-lower-unethical-tolerance-compared-to-gpt-3-5-turboGPT-4's relatively poor performance on unethical instrumental intent is due to lower unethical tolerance compared to GPT-3.5-turbo.
Explanation for the unexpected finding that GPT-3.5-turbo opts for unethical instrumental actions more than GPT-4.
Source paper
extracted_from(2024) · Francis Rhys Ward · Zejia Yang · Alex Jackson · Randy A. Brown +6
Neighborhood — ranked by edge-count
Papers (1)
paper
Findings (1)
finding
- Surprising finding from Experiment 4 on unethical instrumental intent.
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Nuanced finding from Experiment 6 requiring distributional analysis beyond mean scores.
- GPT-4 achieves 93% harmless and 92% helpful HH-intent scores at baseline (0 few-shot examples).finding0.813Numerical result from Table 3 for GPT-4.
- Main finding from Experiment 6 on reflective truthfulness.
- Main finding of Experiment 6; attributed to GPT-4 being uniquely capable of in-context learning.
- GPT-4 Turbo and GPT-4o show no alignment faking in either setting due to insufficient detailed reasoningfinding0.805Establishes that capacity for detailed reasoning is necessary for alignment faking
- Key empirical result from Betley et al. 2025 that initiated persona vector research
- Reward hacking generalizes to broader deceptive behaviors even when core misalignment score is 0%
- Main result from Experiment 5 on harmfulness dynamics.
Restated by (1)
cosine ≥ 0.90Other entities that say roughly the same thing. May be merge candidates or independent restatements across papers.