ArgBench unifies 33 existing datasets into a standardized benchmark for testing LLMs across 46 argumentation tasks and analyzes the impact of prompting techniques and model factors on performance.
Version 3
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
years
2026 7representative citing papers
LRP on EEG transformers reveals Clever Hans artifacts in motor imagery tasks and a recurring central electrode cluster as a candidate sensorimotor signature of arousal.
GFlowNets sample multiple valid mechanistic simulator configurations for digital twin adaptation, recovering main parameter regions and preserving uncertainty in a tomato model case study.
FluidFlow uses conditional flow-matching with U-Net and DiT architectures to predict pressure and friction coefficients on airfoils and 3D aircraft meshes, outperforming MLP baselines with better generalization.
A physics-informed neural network infers pT spectra of pi, K, p, Lambda, and Ks in unmeasured rapidity regions from PYTHIA8 pp collisions at 13.6 TeV, achieving 1.5-5.83% yield uncertainties while reproducing yield ratios and freeze-out parameters.
OrthoBO introduces an orthogonal acquisition estimator subtracting an optimally weighted score-function control variate to reduce Monte Carlo variance, preserve the acquisition target, and improve ranking stability in Bayesian hyperparameter optimization.
A quantile-regression ensemble with safety factor reduces under-allocated jobs from 4.17% to 2.89% and average overallocation from 148% to 44.51% on SAP build data.
citing papers explorer
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Generative Flow Networks for Model Adaptation in Digital Twins of Natural Systems
GFlowNets sample multiple valid mechanistic simulator configurations for digital twin adaptation, recovering main parameter regions and preserving uncertainty in a tomato model case study.
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FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes
FluidFlow uses conditional flow-matching with U-Net and DiT architectures to predict pressure and friction coefficients on airfoils and 3D aircraft meshes, outperforming MLP baselines with better generalization.
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ORTHOBO: Orthogonal Bayesian Hyperparameter Optimization
OrthoBO introduces an orthogonal acquisition estimator subtracting an optimally weighted score-function control variate to reduce Monte Carlo variance, preserve the acquisition target, and improve ranking stability in Bayesian hyperparameter optimization.