Extending Confidence-Based Text2Cypher with Grammar and Schema Aware Filtering
Makbule Gulcin Ozsoy

TL;DR
This paper enhances confidence-based inference for Text2Cypher by incorporating grammar and schema constraints at test time, improving query validity and execution accuracy.
Contribution
It introduces a sequential filtering framework that applies structural constraints during inference, demonstrating improved reliability in Text2Cypher generation.
Findings
Grammar filtering improves syntactic validity.
Schema-aware filtering enhances execution correctness.
Stronger filtering reduces coverage and increases empty outputs.
Abstract
Large language models (LLMs) allow users to query databases using natural language by translating questions into executable queries. Despite strong progress on tasks such as Text2SQL, Text2SPARQL, and Text2Cypher, most existing methods focus on better prompting, fine-tuning, or iterative refinement. However, they often do not explicitly enforce structural constraints, such as syntactic validity and schema consistency. This can reduce reliability, since generated queries must satisfy both syntax rules and database schema constraints to be executable. In this work, we study how structured constraints can be used in test-time inference for Text2Cypher. We focus on post-generation validation to improve query correctness. We extend a confidence-based inference framework with a sequential filtering process that combines confidence scoring, grammar validation, and schema constraints before…
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