2026· Annual Meeting of the Association for Computational Linguistics· pp. 31989-32014· 1 citation· 26 references
Computer Science
TL;DR
GQLBench is a new benchmark built through an automated and scalable framework that integrates NL2SQL-to-NL2GQL conversion with graph-native data generation, and enables fine-grained evaluation across dialects, graph patterns, and query complexity.
Abstract
. Abstract Despite growing interest in NL2GQL, benchmarking progress has been constrained by the lack of resources that are simultaneously large-scale, cross-domain, and cross-dialect. To address this gap, we present GQLBench , a new benchmark built through an automated and scalable framework that integrates NL2SQL-to-NL2GQL conversion with graph-native data generation. GQLBench supports execution-based evaluation on both Cypher and ISO GQL, covering hundreds of graph databases and over 20k natural language questions for each dialect. By combining converted data from mature NL2SQL resources with synthetic graph-specific queries, it captures both schema diversity from real-world relational sources and graph-native reasoning challenges, including long paths and cycles. Beyond overall performance comparison, GQLBench also enables fine-grained evaluation across dialects, graph patterns, and query complexity. Experiments on advanced LLMs show that even strong proprietary models struggle on GQLBench, with gemini-3-flash achieving only 35.40% average execution accuracy across the two di-alects. Our data and code are available at https://github.com/qxssadf/GQLBench .
A large-scale benchmark with 60\sim 90× more queries than prior work, built on 3× more databases, an automated pipeline that can execute existing methods without manual intervention, and multi-dimensional, fine-grained evaluation metrics for comprehensive assessment.
Bo Li, Chenzhan Wang, Long-Kang Lin et al.· Proceedings of the 32nd ACM...· 0 citations
This work introduces ExpeSQL, a zero-shot, open-source–compatible, and efficient framework that combines divide-and-conquer reasoning, Best-of-N candidate selection, and self-critique with experience-guided refinement that establishes a new paradigm for deployable, self-improving Text-to-SQL systems in dynamic, real-world environments.
MGQL is presented, the first mechanized, small-step operational semantics for a substantial read-only fragment of GQL that is grounded in the ISO/IEC 39075 standard, and it is proved that the type system is sound, ensuring an end-to-end guarantee of well-formed queries yielding results that conform to their declared schemas.
A framework for scalable, dataset-scale LLM-based data transformation that processes data row-by-row in batches over lazily evaluated, distributed dataframes, enabling LLM-based transformations on arbitrarily large datasets without loading them into memory.
A. Abraham, F. Rahman, Fadil Rahman et al.· Proceedings of the 15th Inte...· 0 citations
SHACL is a core technology for validating the conformance of RDF knowledge graphs (KGs). Yet, authoring SHACL shapes requires technical expertise that most domain experts lack. Translating natural language requirements into SHACL (NL2SHACL) would lower this barrier. However, there is no dedicated benchmark for NL2SHACL, and evaluating generated shapes requires methods beyond string comparison, as semantically equivalent shapes can differ in serialisation and structure. To tackle these challenges, we present NL2SHACL-Bench, a benchmark suite for natural language to SHACL translation. Using NL2SHACL-Bench, we evaluate four state-of-the-art large language models (LLMs) for this task. Our results show that current LLMs are highly capable of generating syntactically valid SHACL, but still struggle to produce semantically equivalent constraints for complex logical and structural patterns. This indicates that NL2SHACL-Bench provides a meaningful basis for measuring advances in the NL2SHACL state of the art.
We present L3Cube-IndicQuest v2, a large-scale gold-standard multilingual question-answering benchmark for evaluating the India-specific factual knowledge of Large Language Models (LLMs). The benchmark comprises 3,471 curriculum-grounded English question--answer pairs spanning nine domains, curated from educational curricula, competitive examination materials, and domain-specific reference books. We introduce a practical hybrid construction strategy that combines context-grounded LLM-based question generation and validation with semantic deduplication and human verification, enabling scalable creation of benchmark data while preserving annotation quality. The benchmark is translated into 19 Indic languages, yielding a publicly released multilingual dataset of 69,420 question--answer pairs across 20 languages. We evaluate six LLMs under three protocols: LLM-as-a-judge and two deterministic lexical criteria, exact-substring and word-overlap matching. All three produce almost the same model ranking, showing that the results do not depend on the choice of judge. The frontier commercial model leads by a wide margin, and among open-weight models Gemma4 31B outperforms the Indic-specialised Sarvam 30B in every evaluated Indic language.
Rinit Jain, Tirthraj Mahajan, Advait Joshi et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
