LIFT pairs a pre-trained LLM for initial table extraction with a fine-tuned SLM for error repair, matching end-to-end SLM fine-tuning on TEDS while needing only 1,000 examples and gaining robustness.
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Spectrum-adaptive post-hoc generalization bounds for multi-layer Transformers are derived using layerwise Schatten quantities whose indices are chosen after training based on singular-value profiles.
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LIFT: Last-Mile Fine-Tuning for Table Explicitation
LIFT pairs a pre-trained LLM for initial table extraction with a fine-tuned SLM for error repair, matching end-to-end SLM fine-tuning on TEDS while needing only 1,000 examples and gaining robustness.
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Spectrum-Adaptive Generalization Bounds for Trained Deep Transformers
Spectrum-adaptive post-hoc generalization bounds for multi-layer Transformers are derived using layerwise Schatten quantities whose indices are chosen after training based on singular-value profiles.
- Lessons from the Trenches on Reproducible Evaluation of Language Models