Reasoning LLMs with minimal tools for tree construction and analysis induce decision trees that outperform CART, compete with ensembles on low-resource tabular data, and provide human-readable reasoning traces.
Llm meeting decision trees on tabular data
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
AIR excels on label-remapping classification tasks while KNN retrieval leads on closed-book QA and fine-tuning leads on structured extraction and event-order reasoning, showing task-dependent adaptation performance.
citing papers explorer
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Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data
Reasoning LLMs with minimal tools for tree construction and analysis induce decision trees that outperform CART, compete with ensembles on low-resource tabular data, and provide human-readable reasoning traces.
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BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification
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.
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Automated Instruction Revision (AIR): A Structured Comparison of Task Adaptation Strategies for LLM
AIR excels on label-remapping classification tasks while KNN retrieval leads on closed-book QA and fine-tuning leads on structured extraction and event-order reasoning, showing task-dependent adaptation performance.