The Pile is a newly constructed 825 GiB dataset from 22 diverse sources that enables language models to achieve better performance on academic, professional, and cross-domain tasks than models trained on Common Crawl variants.
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A scale-free gain rule applied to variational ELBO paths recovers true factor dimensionality in partially exploratory factor analysis where raw information criteria over-factor.
Biased pay beliefs distort amenity-pay tradeoffs in stated preferences, with full disclosure restoring alignment to full-info benchmarks while short-term info does not.
A new LLM-based system generates and validates runnable modular MCMC samplers directly from natural-language Bayesian model descriptions, reporting success on 120 of 132 benchmark models.
BoolXLLM augments an existing Boolean rule learner with LLMs for feature selection, discretization thresholds, and natural-language rule translation to improve interpretability while preserving accuracy.
Mixtures of convolutional measures on low-dimensional affine spaces admit unique identifiability in semi-parametric settings and posterior contraction rates under convex polytope support assumptions in a well-specified Bayesian regime.
ALLaVA creates 1.3M GPT4V-synthesized samples enabling 4B VLMs to achieve competitive results on 17 benchmarks and match 7B/13B models on some tasks.
Embeddings reliably capture authorial stylistic features in French literary texts, and these signals persist after LLM rewriting while showing model-specific patterns.
Graph-augmented LLMs using a political knowledge graph improve ideology prediction accuracy for Swiss MPs by incorporating relational data beyond text alone.
Large-scale computational comparison of two major Holocaust oral history collections shows both expected differences and significant overlaps in interview structure, yielding a replicable framework for archive analysis.
citing papers explorer
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The Pile: An 800GB Dataset of Diverse Text for Language Modeling
The Pile is a newly constructed 825 GiB dataset from 22 diverse sources that enables language models to achieve better performance on academic, professional, and cross-domain tasks than models trained on Common Crawl variants.
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Recovering Latent Structures after Variational Bayesian Variable Selection: Fit Assessment and Factor-Number Selection in Partially Exploratory Factor Analysis
A scale-free gain rule applied to variational ELBO paths recovers true factor dimensionality in partially exploratory factor analysis where raw information criteria over-factor.
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Pay Beliefs and the Amenity-Pay Tradeoff
Biased pay beliefs distort amenity-pay tradeoffs in stated preferences, with full disclosure restoring alignment to full-info benchmarks while short-term info does not.
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AI4BayesCode: From Natural Language Descriptions to Validated Modular Stateful Bayesian Samplers
A new LLM-based system generates and validates runnable modular MCMC samplers directly from natural-language Bayesian model descriptions, reporting success on 120 of 132 benchmark models.
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BoolXLLM: LLM-Assisted Explainability for Boolean Models
BoolXLLM augments an existing Boolean rule learner with LLMs for feature selection, discretization thresholds, and natural-language rule translation to improve interpretability while preserving accuracy.
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Learning Mixtures of Nonparametric and Convolutional Measures on Effectively Low-dimensional Affine Spaces
Mixtures of convolutional measures on low-dimensional affine spaces admit unique identifiability in semi-parametric settings and posterior contraction rates under convex polytope support assumptions in a well-specified Bayesian regime.
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ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models
ALLaVA creates 1.3M GPT4V-synthesized samples enabling 4B VLMs to achieve competitive results on 17 benchmarks and match 7B/13B models on some tasks.
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Measuring Embedding Sensitivity to Authorial Style in French: Comparing Literary Texts with Language Model Rewritings
Embeddings reliably capture authorial stylistic features in French literary texts, and these signals persist after LLM rewriting while showing model-specific patterns.
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Graph-Augmented LLMs for Swiss MP Ideology Prediction
Graph-augmented LLMs using a political knowledge graph improve ideology prediction accuracy for Swiss MPs by incorporating relational data beyond text alone.
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The Shape of Testimony: A Scalable Framework for Oral History Archive Comparison
Large-scale computational comparison of two major Holocaust oral history collections shows both expected differences and significant overlaps in interview structure, yielding a replicable framework for archive analysis.