Comparison and Analysis of Value Linking in Text-to-SQL systems: [Experiments & Analysis]

Value linking in text-to-SQL systems is the problem of associating values mentioned or implied within a NL query with their database entries. Despite existing efforts, their performance and comparative effectiveness remain unexplored. This work provides the first systematic study focused exclusively on value linking. First, we present a taxonomy that encapsulates the key steps and design choices for value linking. To enable a thorough analysis, we introduce VLD-Bench, a new, challenging benchmark that incorporates 18 categories of lexical and semantic discrepancies between values referenced in the queries and database entries. Our evaluation shows that state-of-the-art text-to-SQL systems suffer up to a 64% drop in execution accuracy due to value linking errors. A fine-grained analysis reveals how design choices impact robustness to linguistic variation and system latency, offering key insights and directions for future research.

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Publication Details

Journal
Proceedings of the ACM on Management of Data
Published
2026-05-18
DOI
https://doi.org/10.1145/3802029
Primary Topic
Advanced Database Systems and Queries
Type
article
Field-Weighted Citation Impact
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article

Comparison and Analysis of Value Linking in Text-to-SQL systems: [Experiments & Analysis]

Georgia Koutrika, George Katsogiannis-Meimarakis, Apostolos Koukouvinis
Proceedings of the ACM on Management of Data
Advanced Database Systems and Queries
article

Comparison and Analysis of Value Linking in Text-to-SQL systems: [Experiments & Analysis]

Georgia Koutrika, George Katsogiannis-Meimarakis, Apostolos Koukouvinis
article en

Abstract

Value linking in text-to-SQL systems is the problem of associating values mentioned or implied within a NL query with their database entries. Despite existing efforts, their performance and comparative effectiveness remain unexplored. This work provides the first systematic study focused exclusively on value linking. First, we present a taxonomy that encapsulates the key steps and design choices for value linking. To enable a thorough analysis, we introduce VLD-Bench, a new, challenging benchmark that incorporates 18 categories of lexical and semantic discrepancies between values referenced in the queries and database entries. Our evaluation shows that state-of-the-art text-to-SQL systems suffer up to a 64% drop in execution accuracy due to value linking errors. A fine-grained analysis reveals how design choices impact robustness to linguistic variation and system latency, offering key insights and directions for future research.

Proceedings of the ACM on Management of DataVol. 4(3)
Athena Research and Innovation Center In Information Communication & Knowledge Technologies (GR), Université Grenoble Alpes (FR)
Openalex Percentile: Top 4%
Advanced Database Systems and Queries
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