Towards Robustness of Text-to-SQL Models Against Adversarial Table Perturbation
The paper addresses the vital issue of robustness in Text-to-SQL models, specifically against adversarial table perturbations (ATP). While previous research predominantly concentrated on perturbations in the natural language (NL) query aspect, this work shifts focus towards the tabular data side. It introduces a new adversarial paradigm, A, targeting perturbations in tabular structures, which are often overlooked but critical in practical applications.
Contributions
The researchers introduce ADVETA, a specialized benchmark designed to evaluate Text-to-SQL models under realistic tabular perturbations. This benchmark encompasses perturbations such as REPLACE (RPL) and ADD, which simulate real-world modifications like renaming columns or adding new ones. Such scenarios are common in dynamic environments where databases evolve due to shifting business needs.
The paper reveals substantial vulnerabilities in state-of-the-art models when faced with these perturbations. Notably, models demonstrated a 53.1% relative performance drop with RPL and 25.6% with ADD. These findings emphasize that current models significantly rely on rigid patterns rather than robust understanding, making them susceptible to these perturbations.
Methodology
To mitigate these vulnerabilities, the authors propose Contextualized Table Augmentation (CTA), an adversarial training framework that expands the training dataset with contextually relevant adversarial examples. Unlike previous techniques, CTA leverages a combination of dense retrieval and reranking, enhanced by Natural Language Inference (NLI), to ensure the semantic relevance and syntactic correctness of augmented examples. This method not only boosts robustness against table perturbations but generalizes well to NL-side disruptions as well.
Numerical Results and Analysis
The performance gains reported illustrate CTA's effectiveness. In comparisons, models trained with CTA augmentation outperformed traditional adversarial techniques, achieving notably higher robustness across diverse datasets like Spider, WikiSQL, and WTQ. Specifically, CTA improved the schema linking F1 score by considerable margins, highlighting its contribution to effective schema recognition—a critical component in Text-to-SQL parsing.
Implications and Future Directions
The research highlights a significant gap in the robustness of current Text-to-SQL parsers, illuminating potential avenues for enhancement via contextual adversarial training. The methodologies and insights can drive future developments in robustness against not only tabular perturbations but also broader data variabilities.
The implications are profound for enterprise environments where database modifications are routine. Ensuring robust query parsers will enhance reliability, facilitating non-expert database interactions and reducing dependency on domain specialists.
Conclusion
This paper makes a substantial contribution to the field by unveiling the vulnerabilities of Text-to-SQL models to ATP while offering a viable solution through CTA. It sets a foundation for further exploration into robust, adaptable parsing methodologies that align with the dynamic nature of real-world databases. As AI and NLP systems become integral to data management processes, such work ensures these systems are not only sophisticated but resilient and dependable in diverse operational settings.