LoID pulls informative priors directly from LLM token predictions instead of generated text, recovering up to 59% of the oracle performance gap on 10 OOD tabular datasets.
arXiv preprint arXiv:2312.16702 , year=
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LLM-TabLogic extracts inter-column logical constraints using LLMs and conditions a score-based latent diffusion model on them to generate synthetic tabular data that preserves those relationships.
TabGRAA applies group-relative advantage alignment in an iterative reward-guided post-training loop to improve tabular language model generators on fidelity, utility, and privacy trade-offs across five benchmarks.
citing papers explorer
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What Language Models Know But Don't Say: Non-Generative Prior Extraction for Generalization
LoID pulls informative priors directly from LLM token predictions instead of generated text, recovering up to 59% of the oracle performance gap on 10 OOD tabular datasets.
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LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion
LLM-TabLogic extracts inter-column logical constraints using LLMs and conditions a score-based latent diffusion model on them to generate synthetic tabular data that preserves those relationships.
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Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training
TabGRAA applies group-relative advantage alignment in an iterative reward-guided post-training loop to improve tabular language model generators on fidelity, utility, and privacy trade-offs across five benchmarks.