MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA
Factual evidence
What the source reports
Research proposes MARS, a scalable framework for combining LLMs with knowledge graphs (KGs) using multi-hop retrieval and SPARQL generation for grounded answers.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 22 October 2026
- Collected by OneBench
- 17 Jul 2026, 20:29 UK
- Original headline
- MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA ↗
Stored source excerpt
arXiv:2607.14561v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong reasoning performance, but their tendency to hallucinate limits their reliability in knowledge-intensive tasks…
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OneBench interpretation
Institutional assessment
So what
Combining LLMs with enterprise knowledge graphs through advanced retrieval mechanisms directly addresses hallucination risk for regulated use cases.
Do what
This research provides a pathway for more reliable and auditable LLM applications by grounding them in structured enterprise data, influencing future architecture decisions for knowledge-intensive AI deployments.