ORiGAMi synthesizes sparse semi-structured mixed-type JSON data using path-encoded autoregressive tokenization and schema constraints, outperforming flattened tabular baselines on 17 of 18 fidelity, detection, and utility metrics while keeping privacy above 96%.
xval: A continuous number encoding for large language models
7 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
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DIPS fine-tunes LLMs to output ordered feasible decision vectors approximating Pareto fronts for constrained bi-objective convex problems, reaching 95-98% normalized hypervolume with 0.16s inference.
FoNE encodes numbers as single tokens via Fourier features and outperforms subword and digit-wise embeddings on addition, subtraction, and multiplication with far less data.
TabICL scales in-context learning to large tabular data via column-then-row attention for row embeddings followed by a transformer, matching TabPFNv2 speed and performance while outperforming it and CatBoost on datasets over 10K samples.
Fused code-value tokenization improves mortality AUROC from 0.891 to 0.915 and other clinical outcome predictions, while certain temporal encodings like event order match or exceed time tokens with shorter sequences.
Triadic Suffix Tokenization groups digits into triads with fixed magnitude suffixes to make order-of-magnitude relationships explicit at the token level for LLMs.
citing papers explorer
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Autoregressive Synthesis of Sparse and Semi-Structured Mixed-Type Data
ORiGAMi synthesizes sparse semi-structured mixed-type JSON data using path-encoded autoregressive tokenization and schema constraints, outperforming flattened tabular baselines on 17 of 18 fidelity, detection, and utility metrics while keeping privacy above 96%.
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Large Language Models as Amortized Pareto-Front Generators for Constrained Bi-Objective Convex Optimization
DIPS fine-tunes LLMs to output ordered feasible decision vectors approximating Pareto fronts for constrained bi-objective convex problems, reaching 95-98% normalized hypervolume with 0.16s inference.
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FoNE: Precise Single-Token Number Embeddings via Fourier Features
FoNE encodes numbers as single tokens via Fourier features and outperforms subword and digit-wise embeddings on addition, subtraction, and multiplication with far less data.
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TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
TabICL scales in-context learning to large tabular data via column-then-row attention for row embeddings followed by a transformer, matching TabPFNv2 speed and performance while outperforming it and CatBoost on datasets over 10K samples.
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Representation Before Training: A Fixed-Budget Benchmark for Generative Medical Event Models
Fused code-value tokenization improves mortality AUROC from 0.891 to 0.915 and other clinical outcome predictions, while certain temporal encodings like event order match or exceed time tokens with shorter sequences.
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A Triadic Suffix Tokenization Scheme for Numerical Reasoning
Triadic Suffix Tokenization groups digits into triads with fixed magnitude suffixes to make order-of-magnitude relationships explicit at the token level for LLMs.
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