It's 2025 -- Narrative Learning is the new baseline to beat for explainable machine learning
This addresses the need for more interpretable AI systems, though it appears incremental as it builds on existing language model advancements.
The paper tackles the problem of explainable machine learning by introducing Narrative Learning, a method where models are defined in natural language and refined through explanatory prompts, showing that it became more accurate than baseline explainable models on 5 out of 6 datasets due to improvements in language models.
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.