TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings
Real-world tables are often heterogeneous, combining numerical features with free-form string columns. Table foundation models (TFMs) excel on numerical tables but require strings to be encoded numerically, while large language models (LLMs) hold semantic knowledge but are not designed for tabular predictions. Most exi...
Félix Lefebvre, David Holzmüller, Gioia Blayer et al.
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