Just How Resilient Are Large Language Models?
4 days ago
- #Neural Networks
- #Fault Tolerance
- #AI Resilience
- Large Language Models (LLMs) are highly resilient to bit flips caused by cosmic rays or hardware failures.
- Neural networks have redundant parameter encoding, allowing them to function even when thousands of parameters are corrupted.
- Critical regions in LLMs include output layers (like Broca's area in the brain) and attention mechanisms, which affect coherence and focus.
- Quantization reduces parameter precision without significantly impacting performance, enabling efficient deployment.
- Targeted corruption can create backdoors, posing security risks, while random corruption leads to mode collapse (repetitive or nonsensical outputs).
- LLMs' resilience mirrors biological brains, suggesting intelligence relies on redundancy and graceful degradation.
- Fault-tolerant AI is valuable for space missions, military applications, and edge computing where repairs are difficult.
- Resilience, rather than precision, may be a defining characteristic of intelligence.