arXiv Machine Learning
STRABLE: Benchmarking Tabular Machine Learning with Strings
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TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders
arXiv:2606. 09323v1 Announce Type: new Abstract: Tabular encoders are usually evaluated inside task-specific end-to-end pipelines, so models from different training paradigms are difficult to compare directly even when they operate on similar tabular signals.
arXiv Machine Learning
Jun 4
Towards Pretraining Text Encoders for TabPFN
arXiv:2606. 04876v1 Announce Type: new Abstract: Tabular foundation models, such as TabPFN, achieve strong performance on tabular datasets with numerical and categorical data, but do not natively handle high-cardinality text features.
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Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks
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Beyond IID: How General Are Tabular Foundation Models, Really?
arXiv:2606. 30410v1 Announce Type: cross Abstract: Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry.
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TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models
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arXiv Machine Learning
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Why Large Language Models Fail at Tabular Prediction
arXiv:2608. 02412v1 Announce Type: new Abstract: Large language models (LLMs) have become the default tool for a remarkable range of tasks, yet they have had conspicuously little success at one of the most common machine learning workloads: predictive analytics over tabular data.