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It is found that neutral framing of behavioural features, reporting the feature value without prescriptive advice, consistently outperforms the prescriptive variants, and that prescriptive feedback steers the median feature value in the advised direction in only 37% of cases despite empirically grounded advice.
This work presents LACE, an AutoML framework that instead searches over complete executable pipeline programs: an evolutionary loop maintains a population of scikit-learn-compatible Python classes, and a large language model acts as the variation operator.
LLM-driven evolutionary search can discover algorithm designs that achieve Pareto-efficient trade-offs difficult to reach through manual design, with SMAC hyperparameter optimization integrated into the evolutionary loop.
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