Chain-of-Table
Google's Chain-of-Table prompts LLMs to iteratively transform a complex table step-by-step to answer questions reliably, extending CoT reasoning to tabular data.
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Operation-by-operation: The LLM generates a sequence of table operations (add column, delete row, group by, etc.) rather than reasoning purely in natural language.
Dynamic chain: Each operation is chosen based on the current table state, so the reasoning chain adapts to what the data reveals as it's transformed.
Strong benchmark gains: Outperforms prior CoT and program-of-thought baselines on WikiTQ, FeTaQA, and TabFact for table QA and fact verification.
Interpretability bonus: The sequence of table transformations is directly inspectable, making failures easier to debug than text-only reasoning chains over tables.