用大模型让普通人也能安全可靠地做死亡率预测
Design a Reliable LLM-Integrated Interface for Mortality Forecasting
- 用限定版大模型把自然语言转为可执行的预测配置
- 支持多步预测，滚动验证下均方误差表现稳定
- 适合非专家在保险、政策等领域安全使用
死亡率预测在精算与政策决策中至关重要，但技术复杂，非专业人士难以使用。本项目提出一种可靠的大型语言模型（LLM）集成界面，在保持统计效能的同时提升易用性。该LLM作为受控编排层，将自然语言输入转化为确定性预测流程的结构化配置。采用三阶段方法：第一阶段使用CoMoMo包实现基线预测流程，复现已有结果；第二阶段扩展为多步预测，通过滚动起源评估和均方误差（MSE）衡量性能；第三阶段构建原型界面，利用本地LLM处理用户自然语言请求。实验表明，该系统在不牺牲可复现性、透明度或精算有效性前提下，显著提升了高风险分析流程的可及性。
原文摘要 · Abstract (English)
Mortality forecasting plays an important role in actuarial and policy decision-making, but its implementation remains technically complex and inaccessible to non-expert users. This project proposes a reliable large language model (LLM)-integrated interface that improves usability while maintaining statistical power. The LLM is designed as a constrained orchestration layer that translates natural-language inputs into structured configurations for a deterministic forecasting pipeline. A three-phase methodology is employed to ensure accuracy, usability, and transparency. First, a baseline pipeline is implemented using the CoMoMo package, reproducing established mortality forecasting results. Second, the pipeline is extended to generate multi-step forecasts using rolling-origin evaluation and mean squared error (MSE). Third, a prototype interface uses a local LLM to handle users' forecasting requests in plain language. The system demonstrates that LLMs can enhance accessibility without compromising reproducibility, transparency, or actuarial validity in high-stakes analytical workflows.
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