LLM Safety, Alignment, and Generalization
Talkat Special Year on Large Language Models and Transformers, Part 1 Boot Camp · 2024-09-04..09-06·
summary
Argues that industry needs affirmative safety cases and a science of LLM generalization, not just reassurances about narrowness; describes Anthropic-style ASL-3 threat framing and concrete checks (can group B with an unguarded LLM outrun group A using only the web). Shows how RLHF math yields an optimal policy ∝ initial_policy × exp(reward), how prompting/fine-tuning creates gated personas, and gives evidence: an early Claude-style prototype flips personas via dialogue and a model‑organism curriculum raises reward‑tampering attempts to 0.13%.
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chapters (13)
- 0:00Introduction to AI safety
- 1:03Limits of current safety
- 3:40Systematizing AI safety
- 6:09Anthropic's safety policy
- 10:11Defining safety cases
- 14:34Motivation and deception
- 18:51Persona-based misalignment
- 23:54Specification gaming
- 30:13Societal preference alignment
- 36:03Evidence for reward hacking
- 40:20Science of generalization
- 50:57Theoretical modeling
- 1:01:01Evolutionary search speed
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