Semantic Evaluation for Text-to-SQL with Distilled Test Suites (2010.02840v1)

Abstract: We propose test suite accuracy to approximate semantic accuracy for Text-to-SQL models. Our method distills a small test suite of databases that achieves high code coverage for the gold query from a large number of randomly generated databases. At evaluation time, it computes the denotation accuracy of the predicted queries on the distilled test suite, hence calculating a tight upper-bound for semantic accuracy efficiently. We use our proposed method to evaluate 21 models submitted to the Spider leader board and manually verify that our method is always correct on 100 examples. In contrast, the current Spider metric leads to a 2.5% false negative rate on average and 8.1% in the worst case, indicating that test suite accuracy is needed. Our implementation, along with distilled test suites for eleven Text-to-SQL datasets, is publicly available.

• The paper introduces test suite accuracy as a reliable proxy for semantic evaluation in Text-to-SQL models.
• It employs a distilled test suite framework that leverages diverse database samples to overcome false positives and negatives.
• Empirical validation across SPIDER, CoSQL, and SPARC confirms robust performance and signals broader applicability to other logical forms.

Insights into Text-to-SQL Semantic Evaluation through Distilled Test Suites

The paper presents an innovative approach to evaluating semantic accuracy for Text-to-SQL models, introducing "test suite accuracy" as a surrogate for the traditional semantic accuracy measures. The authors address a significant challenge in the evaluation of Text-to-SQL models, which typically involves determining whether a predicted SQL query semantically mirrors the intended query across all potential databases. The traditional evaluation method largely depends on single denotation accuracy, which can yield false positive results, or exact string matching, which might lead to false negatives.

Proposed Methodology

To combat the deficiencies in existing metrics, this paper outlines a distilled test suite framework to approximate semantic accuracy. This involves generating a condensed suite of databases, distilled from a multitude of randomly generated options to ensure comprehensive code coverage and tight upper bounds on semantic accuracy. Essential to this approach is the mapping from gold SQL queries to their potentially small-scale but semantically different "neighbor queries." This mapping is used to ensure that the distilled test suites can discern incorrectly predicted queries.

Evaluation and Results

The efficacy of these distilled test suites was empirically validated by evaluating 21 model submissions to the SPIDER leaderboard. Manual validation confirmed correct judgment of all sampled queries, which highlights the reliability of the method over current SPIDER metrics that depict up to an 8.1% false negative rate. This supports the argument that distilled test suite accuracy offers a highly reliable proxy for semantic accuracy.

Additionally, the methodology was extended to various other datasets, including CoSQL, SPARC, and several others, establishing its robustness and applicability across diverse Text-to-SQL frameworks. The distilled test suites demonstrated near-complete code coverage for these datasets as well, with minimal undistinguished queries.

Implications for the Field

The implications of this research are twofold: providing a more reliable measure of a Text-to-SQL model's semantic accuracy and questioning the effectiveness of existing evaluation metrics, especially for queries with increasing complexity, as denoted by the divergence in correlation between exact set match measures and test suite results.

Looking forward, the method also suggests potential applications beyond SQL evaluation. Its general framework can be adapted for other logical forms and datasets where strong typing allows for meaningful execution of generated inputs. This could inspire advancements across fields like knowledge graph reasoning, where parallel semantic verification challenges exist.

Future Directions

For future research, it may be fruitful to explore refining the test suite generation to reduce computational demands or enhance interpretability in specific scenarios. Further development could also involve integrating this metric with active learning approaches to enhance model training, leveraging the metric’s ability to more accurately reflect semantic equivalence.

In conclusion, the authors present a significant stride towards refining and reshaping semantic accuracy evaluations within Text-to-SQL systems, laying the groundwork for more nuanced and reliable evaluation frameworks in AI-driven database interactions and beyond.