godaai/llm-table-survey
Resources on Large Language Models for Table Processing
Curated survey of 200+ papers spanning foundational LLM architectures (GPT-3, T5, FLAN), table-specific pretraining methods (TaBERT, TaPEx), instruction-tuned models (TableLlama), and prompting strategies for NL2SQL and table QA tasks. Organized by technical approach—from pre-LLM era table training through hybrid table-code models and multimodal extraction—with linked implementations, model checkpoints on HuggingFace, and associated benchmarks/datasets for reproducible research.
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