
AI Safety Fundamentals: Alignment
Weak-To-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision
Mar 26, 2024
Guest Collin Burns discusses weak-to-strong generalization in AI alignment, exploring fine-tuning strong models with labels from weaker models to enhance performance. Techniques like auxiliary confidence loss show promise in improving weak-to-strong generalization, suggesting progress in aligning superhuman models with human supervision.
35:05
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Quick takeaways
- Weak-to-strong generalization involves fine-tuning strong models with labels from weak models to boost performance, especially in NLP tasks.
- Simple methods like adding an auxiliary confidence loss can significantly enhance weak-to-strong generalization, bridging performance gaps between weak and strong models.
Deep dives
Weak to Strong Generalization with Weak Supervision
The episode explores the concept of weak to strong generalization, where strong pre-trained models are fine-tuned using labels generated by weaker models to improve performance. It discusses the challenges of aligning superhuman models, highlighting the difficulty of weak supervision for models capable of complex behaviors beyond human comprehension. Results show that naive fine-tuning on weak supervision does lead to improved performance, termed weak to strong generalization, especially in natural language processing tasks.
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