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Table-augmented generation shows promise for complex dataset querying, outperforms text-to-SQL
In tests, all baselines achieved no more than 20% accuracy, while table-augmented generation did far better with 40% or better accuracy.
It is a unified and general-purpose paradigm that represents a wide range of previously unexplored interactions between the language model(LM) and database and creates an exciting opportunity for leveraging the world knowledge and reasoning capabilities of LMs over data, the UC Berkeley and Stanford researchers wrote in a paper detailing TAG. “Database systems provide (only) a source of domain knowledge through the up-to-date data they store, as well as exact computation at scale (which LMs are bad at),” While the approach is new, the results clearly indicate that it can give enterprises a way to unify AI and database capabilities to answer complex questions over structured data sources.
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