A systematic framework modularizes tabular data disentanglement into data extraction, modeling, analysis, and latent extrapolation, with a case study on synthetic data generation.
Beyond neural scaling laws: beating power law scaling via data pruning.Advances in Neural Information Processing Systems, 35:19523–19536, 2022
2 Pith papers cite this work. Polarity classification is still indexing.
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Selecting preference pairs whose DPO implicit reward gap is small yields better LLM alignment than random or baseline selection while using only 10% of the data.
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A Systematic Framework for Tabular Data Disentanglement
A systematic framework modularizes tabular data disentanglement into data extraction, modeling, analysis, and latent extrapolation, with a case study on synthetic data generation.
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Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap
Selecting preference pairs whose DPO implicit reward gap is small yields better LLM alignment than random or baseline selection while using only 10% of the data.