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ARXIV:2603.17017 · NL2SQL EVALUATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.17017NL2SQL EVALUATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A robustness evaluation benchmark for NL2SQL systems addressing real-world noise and linguistic variation.
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Pain A robustness evaluation benchmark for NL2SQL systems addressing real-world noise and linguistic variation.
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A robustness evaluation benchmark for NL2SQL systems addressing real-world noise and linguistic variation. In this work, we introduce a robustness evaluation benchmark containing approximately ten types of perturbations and conduct evaluations under both traditional…
Robustness evaluation for Natural Language to SQL (NL2SQL) systems is essential because real-world database environments are dynamic, noisy, and continuously evolving, whereas conventional benchmark evaluations typically assume static schemas and well-formed user inputs. In…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Our results show that these models generally maintain strong performance under several perturbations; however, notable performance degradation is observed for surface-level noise (e.g., character-level…
NL2SQL Evaluation moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A robustness evaluation benchmark for NL2SQL systems addressing real-world noise and linguistic variation.
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10.48550/arXiv.2603.17017A robustness evaluation benchmark for NL2SQL systems addressing real-world noise and linguistic variation.
Abstract
Robustness evaluation for Natural Language to SQL (NL2SQL) systems is essential because real-world database environments are dynamic, noisy, and continuously evolving, whereas conventional benchmark evaluations typically assume static schemas and well-formed user inputs. In this work, we introduce a robustness evaluation benchmark containing approximately ten types of perturbations and conduct evaluations under both traditional and agentic settings. We assess multiple state-of-the-art large language models (LLMs), including Grok-4.1, Gemini-3-Pro, Claude-Opus-4.6, and GPT-5.2. Our results show that these models generally maintain strong performance under several perturbations; however, notable performance degradation is observed for surface-level noise (e.g., character-level corruption) and linguistic variation that preserves semantics while altering lexical or syntactic forms. Furthermore, we observe that surface-level noise causes larger performance drops in traditional pipelines, whereas linguistic variation presents greater challenges in agentic settings. These findings highlight the remaining challenges in achieving robust NL2SQL systems, particularly in handling linguistic variability.
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PROBLEM
A robustness evaluation benchmark for NL2SQL systems addressing real-world noise and linguistic variation. In this work, we introduce a robustness evaluation benchmark containing approximately ten types of perturbations and conduct evaluations under both traditional and agentic...
METHOD
Robustness evaluation for Natural Language to SQL (NL2SQL) systems is essential because real-world database environments are dynamic, noisy, and continuously evolving, whereas conventional benchmark evaluations typically assume static schemas and well-formed user inputs. In this...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Our results show that these models generally maintain strong performance under several perturbations; however, notable performance degradation is observed for surface-level noise (e.g., character-level co...
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NL2SQL Evaluation moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A robustness evaluation benchmark for NL2SQL systems addressing real-world noise and linguistic variation. In this work, we introduce a robustness evaluation benchmark containing approximately ten types of perturbations and conduct evaluations under both traditional and agentic settings.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Robustness evaluation for Natural Language to SQL (NL2SQL) systems is essential because real-world database environments are dynamic, noisy, and continuously evolving, whereas conventional benchmark evaluations typically assume static schemas and well-formed user inputs. In this work, we introduce a robustness evaluation benchmark containing approximately ten types of perturbations and conduct evaluations under both traditional and agentic settings.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Our results show that these models generally maintain strong performance under several perturbations; however, notable performance degradation is observed for surface-level noise (e.g., character-level corruption) and linguistic variation that preserves semantics while altering lexical or syntactic forms.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
NL2SQL Evaluation moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A robustness evaluation benchmark for NL2SQL systems addressing real-world noise and linguistic variation.
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NL2SQL Evaluation
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