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arXiv Computation and Language By Yumiao Li, Peixin Liu, Donglin Di, Chen Li, Runhuan Feng

Can Large Language Models Anticipate Behavioral Responses to Social Policies? A Case of Pension Enrollment Prediction among China's Flexible Workers

Read the original on arXiv Computation and Language →

The paper introduces FlexPension-LLM, a domain‑specialized large language model designed to predict pension enrollment among China’s flexible workers. By injecting policy‑grounded cues and using LoRA/SFT for rationale‑augmented supervision, the model achieves a Composite F1 score of 0.9316 on a blind split, outperforming several baselines and matching top commercial models. External survey tests confirm its robustness, with the narrowest performance range among strong systems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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