LLMs for Table Processing: A Survey
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A survey covering how LLMs and VLMs are used across the full spectrum of table-processing tasks, from classic TableQA to spreadsheet manipulation.
Task coverage: Spans table QA, fact-checking, table-to-text, spreadsheet operations, and table-centric data analysis, unifying what are usually studied as separate subfields.
Method taxonomy: Organizes training techniques (instruction tuning, pretraining on table-augmented text), prompting strategies, and LLM-agent architectures specific to tables.
Evaluation landscape: Catalogs datasets, benchmarks, and metrics for each task family so practitioners can compare systems without re-reading the whole literature.
Open problems: Identifies open challenges including heterogeneous input formats, long/large tables, and reasoning efficiency as the main frontiers.
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