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This research demonstrates a practical approach to bridging the gap between complex actuarial forecasting tools and non-expert users by leveraging a constrained LLM as an orchestration layer. The system successfully translates natural language inputs into deterministic pipelines without sacrificing statistical rigor or reproducibility. For a technical audience, the key takeaway is the viability of using local LLMs to enhance accessibility in high-stakes domains while maintaining strict adherence to established validation metrics like MSE.
Design a Reliable LLM-Integrated Interface for Mortality Forecasting
arXiv:2606.06235v1 Announce Type: cross Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.06235 [cs.LG]
(or arXiv:2606.06235v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.06235 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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