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LLM FoundationsVery Hard· Model Architecture· Performance Optimization· Trade-offs
Platform wants to drop TableFormer and go back to TAPAS...
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See plansBefore switching, verify if the attention bias truly blocks fused-kernel exports, as it is often compatible. If the constraint is real, compare the costs: 5x augmentation consumes significant training compute for only a tiny fraction of the possible row permutations, providing no formal invariance. A better synthesis is to canonicalize row order in the parser to fix crawl-to-crawl churn, keep augmentation for residual robustness, and track Variation of Prediction (VP) in service SLOs to monitor future extractor performance.