Syntax vs. Semantics: How Transformers Learn Deep Dependencies
Quick summary
arXiv:2608.26139v1 Announce Type: cross Abstract: Large Language Models demonstrate remarkable syntactic fluency, yet the optimization dynamics governing their acquisition of deep semantic dependencies remain poorly understood. We propose a mechanistic framework that models this learning process as a competition between Surface Statistics and Deep Semantics. Our theoretical analysis identifies a ``Gradient Starvation" phenomenon where the error signals for sparse semantic dependencies are actively suppressed during early optimization. This suppression impedes the learning of structural reasoni
Key takeaways
- arXiv:2608.26139v1 Announce Type: cross Abstract: Large Language Models demonstrate remarkable syntactic fluency, yet the optimization dynamics governing their acquisition of deep semantic dependencies remain poorly understood.
- We propose a mechanistic framework that models this learning process as a competition between Surface Statistics and Deep Semantics.
- Our theoretical analysis identifies a ``Gradient Starvation" phenomenon where the error signals for sparse semantic dependencies are actively suppressed during early optimization.
Why it matters
“Syntax vs. Semantics: How Transformers Learn Deep Dependencies” signals where capital and distribution power are moving in the AI market. Product continuity, pricing, workforce skills and the competitive options available to startups may all be affected.
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