📜

Training goals for large language models

📅 2022
policy development
🔵 Rare
#ai #decision theory #language models #oracle ai #self fulfilling/refuting prophecies #seri mats

📖 Description

*This post was written under Evan Hubinger's mentorship, as part of the*[*Stanford Existential Risks Initiative ML Alignment Theory Scholars (SERI MATS) program*](https://www.lesswrong.com/posts/8vLvpxzpc6ntfBWNo/seri-ml-alignment-theory-scholars-program-2022)*. Many of the ideas in this post, including the main idea behind the training goal, are due to Kyle McDonell and Laria Reynolds. In addition, I am grateful for comments and feedback from Arun Jose (who wrote a related post on*[*conditioning generative models for alignment*](https://www.alignmentforum.org/posts/JqnkeqaPseTgxLgEL/conditioning-generative-models-for-alignment)*) and Caspar Oesterheld, and for a helpful discussion with James Lucassen.*

📊 Game Impacts

Variable Change Condition
Research +10 Always
Vibey Doom +5 Always
Ethics Risk -5 Always

💭 Reactions

🔬 Safety Researcher Reaction: ⚠️ Placeholder - Needs Real Quote
"Critical insights for the field"
📰 Media Reaction: ⚠️ Placeholder - Needs Real Quote
"Discussed in AI safety community"
💡 Found a Real Quote? Suggest it here

🔗 Sources

🏷️ Event Metadata

Think this event's metadata could be improved? Suggest changes to category, rarity, tags, game impacts, or p(doom) effects.

🤝 Found an Issue?

This event data is sourced from the pdoom-data repository. If you notice errors or want to suggest improvements:

GitHub Issue (Preferred) 📧 Email (No GitHub)
← Back to All Events