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

Researchers propose DCRC, a data-centric framework addressing numerical hallucinations in LLM-based financial question-answering systems. The approach combines adversarial data construction, multi-stage training, and executable reasoning programs to improve reliability in high-stakes financial applications where accuracy is critical.

Numerical hallucinations in large language models represent a fundamental reliability gap in financial applications where precision directly impacts investment decisions and risk assessment. The DCRC framework tackles this through a paradigm shift from model-centric optimization toward data-centric engineering, recognizing that training data quality and structure matter as much as algorithmic sophistication.

Financial QA systems inherently struggle because they must perform complex numerical reasoning while maintaining audit trails and handling noisy real-world data. Traditional retrieval-augmented generation approaches fail to address these interconnected challenges systematically. The framework's three-phase approach—adversarial data construction, staged agent training, and program synthesis—creates a verifiable chain from question to answer, enabling both computational accuracy and transparency.

The Data-centric Structuring Agent explicitly transforms unstructured financial information into executable reasoning programs, introducing a critical layer of accountability absent in standard LLM pipelines. This design choice directly reduces hallucination risk by replacing probabilistic token generation with deterministic calculation verification.

For the financial technology and AI infrastructure sectors, this represents a maturation pathway for deploying LLMs in regulated, high-stakes environments. Real-world deployment validation signals practical viability beyond academic benchmarks. As financial institutions increasingly adopt AI-driven analysis, systems demonstrating auditability and numerical reliability gain competitive advantage. The approach potentially influences how other sectors requiring numerical precision—healthcare, engineering, legal compliance—structure LLM applications, establishing data-centric frameworks as essential for trustworthy AI deployment.

• →DCRC framework combines adversarial training data construction with executable reasoning programs to eliminate numerical hallucinations in financial QA systems
• →Data-centric paradigm proves more effective than model-centric optimization for addressing interconnected challenges in retrieval-augmented generation
• →Explicit evidence auditing and program synthesis provide verifiable reasoning chains critical for high-stakes financial applications
• →Real-world deployment in operational financial QA system validates framework effectiveness beyond offline benchmarks
• →Approach establishes practical pathway for deploying trustworthy LLMs in regulated financial and compliance-sensitive domains