Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing (2206.14017v2)

Abstract: The importance of building text-to-SQL parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i.e., properly recognizing mentions of unseen columns or tables when generating SQLs. In this work, we propose a novel framework to elicit relational structures from large-scale pre-trained LLMs (PLMs) via a probing procedure based on Poincar\'e distance metric, and use the induced relations to augment current graph-based parsers for better schema linking. Compared with commonly-used rule-based methods for schema linking, we found that probing relations can robustly capture semantic correspondences, even when surface forms of mentions and entities differ. Moreover, our probing procedure is entirely unsupervised and requires no additional parameters. Extensive experiments show that our framework sets new state-of-the-art performance on three benchmarks. We empirically verify that our probing procedure can indeed find desired relational structures through qualitative analysis. Our code can be found at https://github.com/AlibabaResearch/DAMO-ConvAI.

• The paper introduces a novel unsupervised probing method for schema linking that bypasses labeled data using a two-step masked language modeling approach.
• The paper employs hyperbolic geometry with a Poincaré distance metric to accurately measure semantic relevance, outperforming traditional Euclidean methods.
• The paper demonstrates state-of-the-art performance improvements on benchmarks like Spider, SYN, and DK, achieving gains up to 13.5% in some cases.

Overview of "Proton: Probing Schema Linking Information from Pre-trained LLMs for Text-to-SQL Parsing"

The paper "Proton: Probing Schema Linking Information from Pre-trained LLMs for Text-to-SQL Parsing" presents a novel approach aimed at enhancing the generalization capabilities of text-to-SQL parsers. The core contribution of this work is a framework, Proton, which extracts relational structures between natural language (NL) queries and database schemas from large-scale pre-trained LLMs (PLMs). This framework addresses the crucial challenge of schema linking—aligning entity references in NL queries to their corresponding database schema columns or tables, a particularly persistent issue when dealing with unseen databases.

Key Contributions

1. Unsupervised Probing for Schema Linking:

The authors propose a probing methodology that effectively elicits semantic correspondences from PLMs without requiring additional labeled data or parameters. Their approach leverages a two-step masked LLMing process to quantify the relationship between NL tokens and schema items, capturing underlying relational structures robustly.

1. Utilization of Hyperbolic Geometry:

The framework employs a Poincaré distance metric instead of the conventional Euclidean space to better handle complex relational structures. This adaptation allows more precise measurement of semantic relevance, significantly enhancing the capacity for schema linking.

1. Improving Performance Across Benchmarks:

Extensive experimentation reveals that integrating Proton into existing graph-based text-to-SQL parsers, such as RAT-SQL and LGESQL, leads to state-of-the-art performance on several benchmarks, including Spider, SYN, and DK. Notably, improvements up to 7.9% and 13.5% were achieved in DK and SYN benchmarks, respectively.

Implications and Future Directions

The implications of this research are twofold:

• Practical Applications: The Proton framework's ability to generalize across domains implies broader applicability in real-world scenarios where exact schema matching is not feasible. This characteristic is crucial for deploying text-to-SQL systems in diverse, dynamic environments.
• Theoretical Insights: By demonstrating the efficacy of hyperbolic space for semantic probing in PLMs, this paper opens avenues for further research into geometric representations in natural language processing tasks.

Looking forward, the methodology proposed in this paper could be extended to other tasks requiring structured knowledge reasoning, such as knowledge-based question answering and complex dialogue systems. Future research might explore deeper integrations of geometric methods in representation learning or adapt the probing technique to other forms of implicit knowledge within PLMs. The insights gained could significantly influence the design of more sophisticated models capable of handling more nuanced understanding and reasoning tasks.

In summary, the Proton framework offers a robust solution to the critical challenge of schema linking in text-to-SQL parsing and sets a precedent for future work leveraging the hidden potentials of PLMs through innovative probing techniques.