用自然语言定义模型，通过解释性提示迭代优化分类标准。
It's 2025 -- Narrative Learning is the new baseline to beat for explainable machine learning
- 模型全程用自然语言构建，靠解释性提示优化而非传统数值训练。
- 在6个数据集中的5个上，2025年前已超越7种可解释模型的准确率。
- 适合关注模型可解释性与自然语言交互的研究者与应用开发者。
本文提出叙事学习（Narrative Learning）方法，即模型完全以自然语言形式定义，并通过解释性提示迭代优化分类标准，而非依赖传统数值优化。我们通过3个合成数据集和3个真实数据集进行实验，评估该方法的准确性与潜力，并与7种基线可解释机器学习模型对比。结果表明，在6个数据集中的5个上，叙事学习在2025年或更早已实现比基线模型更高的准确率，这得益于语言模型的持续进步。此外，我们还分析了模型输出的词汇统计趋势，作为解释可理解性的代理指标。
原文摘要 · Abstract (English)
In this paper, we introduce Narrative Learning, a methodology where models are defined entirely in natural language and iteratively refine their classification criteria using explanatory prompts rather than traditional numerical optimisation. We report on experiments to evaluate the accuracy and potential of this approach using 3 synthetic and 3 natural datasets and compare them against 7 baseline explainable machine learning models. We demonstrate that on 5 out of 6 of these datasets, Narrative Learning became more accurate than the baseline explainable models in 2025 or earlier because of improvements in language models. We also report on trends in the lexicostatistics of these models' outputs as a proxy for the comprehensibility of the explanations.
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