TabBench-LLM, a benchmark introduced by the TabArena team, evaluates large language models as few-shot in-context tabular classifiers against Random Forest and TabPFN v2, using 19 synthetic tasks and …
Tabular foundation models (TFMs) such as TabPFN from PriorLabs and TabFM from Google are now outperforming gradient-boosted trees like XGBoost, CatBoost, and LightGBM on the TabArena benchmark, challe…
Google Research released TabFM on July 1, a foundation model for tabular data that achieves zero-shot classification and regression with no training or hyperparameter tuning, beating Optuna-tuned XGBo…
Google released TabFM on July 1, a zero-shot foundation model for tabular data that generates predictions without training on the user's dataset, using in-context learning. The model achieves state-of…