Data-centric Compilation Reduces LLM Hallucinations in Financial QA
A new framework called the Data-centric Reasoning Compiler (DCRC) targets numerical hallucinations in large language models (LLMs) for financial question answering (FinQA). The approach addresses three persistent challenges in retrieval-augmented generation (RAG): noise sensitivity, calculation fragility, and auditability crisis. DCRC operates through three phases: adversarial data construction, which synthesizes training examples to improve robustness. The work, published on arXiv (2605.31064), proposes a data-centric paradigm shift away from model-centric methods that optimize retriever or generator in isolation. The framework aims to enhance reliability in high-stakes financial applications where numerical reasoning errors are critical.
Key facts
- DCRC stands for Data-centric Reasoning Compiler
- The framework targets numerical hallucinations in LLMs
- It addresses noise sensitivity, calculation fragility, and auditability crisis
- DCRC uses adversarial data construction as one of its phases
- The work is published on arXiv with ID 2605.31064
- It focuses on financial question answering (FinQA)
- The approach is data-centric rather than model-centric
- RAG (retrieval-augmented generation) is the baseline method
Entities
Institutions
- arXiv