TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings
Most existing bridges use LLMs as plug-in encoders for cell strings, yet how to extract representations effectively for tabular learning remains underexplored.
ProofPaper ↗
Key points
- 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.
- We show that the interface between LLMs and tables matter as much as the LLM itself.
- TILE (Table-Interfaced LLM Embeddings) combines four ingredients: row serialization to provide cross-column context, intermediate-layer extraction to avoid layers specialized for next-token prediction, columnar pooling to remove mean pooling's length bias, and by-pass features to route numbers and categories past the LLM.
Sources (1)
- [1]TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with StringsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 04:29 PM
Most existing bridges use LLMs as plug-in encoders for cell strings, yet how to extract representations effectively for tabular learning remains underexplored.
Real-world tables are often heterogeneous, combining numerical features with free-form string columns.
Extractive summary: sentences quoted from the sources.
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