TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning
DGX agentarXiv:2606.11640v1 Announce Type: cross Abstract: Few-shot tabular learning provides a cost-effective approach for real-world applications where annotation is costly and collecting sufficient samples