A tabular foundation model with LLM-as-Observer features predicts AI agent decisions in controlled games, outperforming baselines by 4 AUC points and 14% lower error at K=16 interactions.
LLM embeddings for deep learning on tabular data
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LLM-based semantic encoding of tabular variables creates schema-adaptive embeddings that support zero-shot transfer and improve multimodal dementia diagnosis on NACC and ADNI datasets.
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Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling
A tabular foundation model with LLM-as-Observer features predicts AI agent decisions in controlled games, outperforming baselines by 4 AUC points and 14% lower error at K=16 interactions.
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Schema-Adaptive Tabular Representation Learning with LLMs for Generalizable Multimodal Clinical Reasoning
LLM-based semantic encoding of tabular variables creates schema-adaptive embeddings that support zero-shot transfer and improve multimodal dementia diagnosis on NACC and ADNI datasets.