The wall confronting large language models
7 days ago
- #Scaling Laws
- #Large Language Models
- #Artificial Intelligence
- Scaling laws limit large language models' (LLMs) ability to improve prediction uncertainty.
- Raising LLM reliability to scientific standards is intractable due to inherent limitations.
- LLMs' learning power comes from generating non-Gaussian outputs from Gaussian inputs, which may cause error pileup and degenerative AI behavior.
- The tension between learning and accuracy contributes to low scaling component values.
- Spurious correlations in large datasets exacerbate the problem, as identified by Calude and Longo.
- Avoiding degenerative AI pathways requires prioritizing insight and understanding of problem structures.