Fighting Numerical Hallucinations via Data-centric Compilation for Online Financial QA

ArXi:2605.31064v1 Announce Type: cross Large Language Models (LLMs) have significantly advanced online data services, particularly in the domain of financial question answering (FinQA). However, such systems remain susceptible to numerical reasoning hallucinations, which critically undermine reliability in high-stakes financial applications. Although retrieval-augmented generation (RAG) has been widely adopted to ground responses in external knowledge, it