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MT-TeQL: Evaluating and Augmenting Neural NLIDB on Real-world Linguistic and Schema Variations

Summary: MT-TeQL applies metamorphic testing to NLIDBs, with semantics-preserving utterance/schema transformations to expose robustness gaps. Across 9 NLIDBs on 62,430 inputs, 15,433 defects; MT-TeQL variants reduce errors by 46.5% without lowering accuracy. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12931
Venue
VLDB
Year
2022
Pagerank
7.0366785e-05
Overall Rank
3,501 | 75.65%
DOI
10.14778/3494124.3494139

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