让逆问题优化过程可解释，用自然语言描述优化步骤。
xInv: Explainable Optimization of Inverse Problems
- 在可微仿真器中注入语言事件，记录优化过程
- 通过大模型将事件序列转为人类可读解释
- 适用于医疗、气候等领域的专家理解优化逻辑
逆问题在医疗、气候科学、农业等领域具有核心作用，通常通过迭代优化求解输入以匹配目标输出。尽管前向模型的可解释性已有进展，逆问题的优化过程仍对领域专家不透明。本文提出一种方法：在可微仿真器中嵌入自然语言事件，于前向与反向传播时生成事件记录；后续利用语言模型将这些事件序列转化为人类可理解的解释。我们在一个示例优化问题和神经网络训练案例中验证了该方法的有效性，使复杂优化过程变得可读、可追踪。
原文摘要 · Abstract (English)
Inverse problems are central to a wide range of fields, including healthcare, climate science, and agriculture. They involve the estimation of inputs, typically via iterative optimization, to some known forward model so that it produces a desired outcome. Despite considerable development in the explainability and interpretability of forward models, the iterative optimization of inverse problems remains largely cryptic to domain experts. We propose a methodology to produce explanations, from traces produced by an optimizer, that are interpretable by humans at the abstraction of the domain. The central idea in our approach is to instrument a differentiable simulator so that it emits natural language events during its forward and backward passes. In a post-process, we use a Language Model to create an explanation from the list of events. We demonstrate the effectiveness of our approach with an illustrative optimization problem and an example involving the training of a neural network.
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