Seed Diffusion solves the long-range dependency problem through the following mechanism:
- Parallel global decoding: Consider all token relationships in the first round of generation, avoiding the word-by-word accumulation of errors in traditional models.
- Diffuse refinement: Gradual correction of early generation errors through multiple rounds of noise removal process, similar to the refactoring process used by humans when writing code
- Structured attention: impose stronger constraints at code syntax nodes (e.g., function boundaries/loop bodies)
Empirical data shows that in generating 200+ lines of code:
- Variable Referencing Accuracy Improvement 63%
- Interface consistency retention of 92%
Recommendations for use: For complex system design, useModule generation + automatic assemblystrategy, first generating the class architecture then populating the method implementation.
This answer comes from the articleSeed Diffusion: Validating High-Speed Language Models for Next-Generation ArchitecturesThe
May not be reproduced without permission:AI productivity tools " How to Eliminate Context Forgetting in Autoregressive Models for Long Code Generation?
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