STRuCT-LLM: Unifying Tabular and Graph Reasoning with Reinforcement Learning for Semantic Parsing

Josefa Lia Stoisser, Marc Boubnovski Martell, Lawrence Phillips, Casper Hansen, Julien Fauqueur

Track: long paper (up to 10 pages)

Keywords: Reinforcement Learning, LLMs, Text-to-SQL, Text-to-Cypher, Structural Transfer

TL;DR: Our research shows that joint training of language models on Text-to-SQL and Text-to-Cypher tasks with reinforcement learning improves performance through structural transfer, enhancing accuracy in both tasks and related structured data reasoning.

Abstract: Large language models (LLMs) can parse natural language into SQL or Cypher, but remain fragmented—lacking a unified ability to reason across both relational and graph-structured data. We present STRuCT-LLM, a reinforcement learning framework for cross-domain query understanding. Our approach integrates supervised chain-of-thought traces with topology-aware execution rewards, enabling models to acquire complementary reasoning skills: computational and inter-column analysis from SQL and graph traversal from Cypher. On noisy real-world datasets (e.g., SEDE), STRuCT-LLM achieves consistent gains over supervised fine-tuning baselines, including 17\% fewer logical errors and 20\% fewer data-reference errors, while maintaining robustness under perturbations. Beyond benchmark improvements, we provide a structural analysis of SQL–Cypher equivalence and qualitative case studies showing how unified training resolves errors that single-domain models cannot. These results establish reinforcement learning as a driver of structure-aware generalization across heterogeneous data modalities, paving the way for natural language interfaces to more diverse and unified database systems.

Presenter: ~Josefa_Lia_Stoisser1

Format: Yes, the presenting author will attend in person if this work is accepted to the workshop.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Funding: No, the presenting author of this submission does *not* fall under ICLR’s funding aims, or has sufficient alternate funding.

Submission Number: 54