Hybrid token-based binning for numeric values in EHR transformers is more robust than explicit interaction modeling, with optimal bin count following an empirically derived power-law in dataset size.
NeurIPS 2023 AI for Science Workshop , year=
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A structured survey of LLM mathematical reasoning that unifies dataset taxonomies, reviews architectures and training strategies, and highlights the gap between answer accuracy and process-level verification.
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How Should Transformers Encode Numeric Values in Electronic Health Records?
Hybrid token-based binning for numeric values in EHR transformers is more robust than explicit interaction modeling, with optimal bin count following an empirically derived power-law in dataset size.
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Mathematical Reasoning in Large Language Models: Benchmarks, Architectures, Evaluation, and Open Challenges
A structured survey of LLM mathematical reasoning that unifies dataset taxonomies, reviews architectures and training strategies, and highlights the gap between answer accuracy and process-level verification.