LLM embeddings from policy text outperform hand-engineered features in a GLM for French motor insurance claim frequency, with larger gains at small sample sizes and further improvement from insurance-specific fine-tuning.
Enhancing actuarial non-life pricing models via transformers.European Actuarial Journal, 14:991–1012
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TabPFN shows temporal specialization where one attention head dominates causal necessity at shifting peak layers depending on task complexity, while contrastive activation steering fails to transfer across samples due to context-dependent attention.
TabPFN-2.5 scales tabular foundation models to 20x larger datasets, outperforms tuned tree models on TabArena, achieves near-perfect win rates against default XGBoost, and adds a distillation engine for fast production deployment.
citing papers explorer
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Semantic insurance pricing with large language models
LLM embeddings from policy text outperform hand-engineered features in a GLM for French motor insurance claim frequency, with larger gains at small sample sizes and further improvement from insurance-specific fine-tuning.
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Where Computation Lives Inside TabPFN: Causal Localisation of Attention Head Function
TabPFN shows temporal specialization where one attention head dominates causal necessity at shifting peak layers depending on task complexity, while contrastive activation steering fails to transfer across samples due to context-dependent attention.
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TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
TabPFN-2.5 scales tabular foundation models to 20x larger datasets, outperforms tuned tree models on TabArena, achieves near-perfect win rates against default XGBoost, and adds a distillation engine for fast production deployment.