BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification

ArXi:2605.06117v1 Announce Type: new Large language models (LLMs) have recently been adapted to tabular prediction by serializing structured features into natural language, but their performance in low-data regimes remains limited compared to gradient-boosted decision trees (GBDTs). In this work, we revisit the boosting paradigm, traditionally associated with tree ensembles, and ask whether it can be applied as a general