Benchmarking and Improving Text-to-SQL Generation under Ambiguity (2023.emnlp-main)
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| Challenge: | Existing decoding algorithms treat SQL queries as a string and produce unhelpful token-level diversity in the top-k. |
| Approach: | They propose a benchmarking algorithm that generates all SQLs in top-k ranked outputs . they use plan-based template generation and constrained infilling to bridge this gap . |
| Outcome: | The proposed algorithm is 2.5 times more effective than state-of-the-art models at generating all candidate SQLs in the top-k ranked outputs. |
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