FinReflectKG -- HalluBench: GraphRAG Hallucination Benchmark for Financial Question Answering Systems
As organizations increasingly integrate AI-powered question-answering systems into financial information systems for compliance, risk assessment, and decision support, ensuring the factual accuracy of AI-generated outputs becomes a critical engineering challenge. Current Knowledge Graph (KG)-augmented QA systems lack systematic mechanisms to detect hallucinations - factually incorrect outputs that undermine reliability and user trust. We introduce FinBench-QA-Hallucination, a benchmark for evaluating hallucination detection methods in KG-augmented financial QA over SEC 10-K filings. The dataset contains 755 annotated examples from 300 pages, each labeled for groundedness using a conservat...
| Authors | Mahesh Kumar, Bhaskarjit Sarmah, Stefano Pasquali |
|---|---|
| Primary category | cs.CL |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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