用可解释概念门控机制，让模型专攻台风等极端天气预测
Target Concept Tuning Improves Extreme Weather Forecasting
- 通过稀疏自编码器和反事实分析自动发现故障相关气象概念
- 仅在特定概念激活时更新参数，提升台风预测精度且不牺牲整体性能
- 识别出有物理意义的环流模式，帮助理解模型偏差
气象预报中的深度学习模型在台风等罕见但高影响事件上表现不佳，因相关数据稀缺。现有微调方法常在忽略极端事件与过拟合之间权衡。本文提出TaCT，一种可解释的概念门控微调框架，实现选择性模型优化：仅针对故障案例进行适应，同时保持常规场景性能。TaCT利用稀疏自编码器与反事实分析自动发现故障相关内部概念，并仅在对应概念激活时更新参数，而非全局调整。实验表明，该方法在多个区域均显著提升台风预测效果，且不影响其他气象变量。所识别概念对应真实的物理环流模式，揭示模型偏差，支持科学预报任务中的可信适应。代码已开源。
原文摘要 · Abstract (English)
Deep learning models for meteorological forecasting often fail in rare but high-impact events such as typhoons, where relevant data is scarce. Existing fine-tuning methods typically face a trade-off between overlooking these extreme events and overfitting them at the expense of overall performance. We propose TaCT, an interpretable concept-gated fine-tuning framework that solves the aforementioned issue by selective model improvement: models are adapted specifically for failure cases while preserving performance in common scenarios. To this end, TaCT automatically discovers failure-related internal concepts using Sparse Autoencoders and counterfactual analysis, and updates parameters only when the corresponding concepts are activated, rather than applying uniform adaptation. Experiments show consistent improvements in typhoon forecasting across different regions without degrading other meteorological variables. The identified concepts correspond to physically meaningful circulation patterns, revealing model biases and supporting trustworthy adaptation in scientific forecasting tasks. The code is available at https://anonymous.4open.science/r/Concept-Gated-Fine-tune-62AC.
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