BoostLLM trains sequential PEFT adapters in a boosting framework with tree path inputs to improve LLM performance on few-shot tabular classification, matching or exceeding XGBoost.
Optimized feature generation for tabular data via llms with decision tree reasoning.Advances in neural information processing systems, 37:92352–92380
2 Pith papers cite this work. Polarity classification is still indexing.
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