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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GiVA uses gradients to initialize vector adapters so they match LoRA performance at eight times lower rank while keeping extreme parameter efficiency.
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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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GiVA: Gradient-Informed Bases for Vector-Based Adaptation
GiVA uses gradients to initialize vector adapters so they match LoRA performance at eight times lower rank while keeping extreme parameter efficiency.