arXiv AI
Scalable Source Selection for Machine Learning Tasks
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arXiv AI
Oct 28
Dr. Post-Training: A Data Regularization Perspective on LLM Post-Training
The paper introduces Dr. Post-Training, a framework that treats general training data as a data‑induced regularizer to prevent overfitting to scarce target objectives, rather than merely a pool for selection. It constructs a feasible set of model update directions from general data and projects target‑driven updates onto this set, unifying standard training and existing data‑selection methods as special cases along a bias‑variance spectrum. Experiments on SFT, RLHF, and RLVR demonstrate that these methods outperform both standard training and state‑of‑the‑art data‑selection baselines, especially when general and target data are highly misaligned, while system optimizations keep overhead minimal.
arXiv AI
Oct 15
Pick the Mixture, Not the Samples: Combinatorial Data Selection for LLMs
arXiv:2610.14028v1 Announce Type: cross Abstract: Data selection for LLM fine-tuning faces two facts about real training: data pools draw from many distinct domains, and evaluation spans diverse task...
arXiv Machine Learning
Oct 12
MetaJoin: Adaptive Join Selections for Heterogeneous Tables
MetaJoin is a label‑free framework that adaptively selects join methods for heterogeneous tables by first choosing an initial specialist based on input evidence and then validating or repairing the result using specialist‑specific output evidence. It introduces MNN Join, a transformation specialist that finds string transformations by maximizing mutual‑nearest‑neighbor coverage and uses reciprocal validation. Evaluated on 670 datasets, MetaJoin achieves a macro row‑F1 of 0.817, surpassing a benchmark oracle and outperforming other label‑free policies by over 0.13, while MNN Join dominates all transformation baselines on 609 transformation‑heavy datasets.
arXiv Computation and Language
Aug 21
EvoSelect: Data-Efficient LLM Evolution for Targeted Task Adaptation
arXiv:2604. 26170v2 Announce Type: replace Abstract: Adapting large language models (LLMs) to a targeted task efficiently and effectively remains a fundamental challenge.
arXiv Machine Learning
Oct 16
INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples
INCONVENIENT is a new benchmark that evaluates how machine‑learning models, especially large language models, generalize when trained on convenience samples that bias feature–target relationships. Unlike prior distribution‑shift tests that rely on natural domain splits, INCONVENIENT explicitly perturbs these relationships while keeping the deployment population unchanged, allowing controlled study of shift severity and direction. The benchmark covers 200 tabular classification and regression datasets and produces about 15,500 shifted training distributions, revealing significant performance degradation.
arXiv Machine Learning
Jul 24
DataPrep-Bench: Benchmarking LLMs as Training Data Preparators
arXiv:2607. 20465v1 Announce Type: new Abstract: The quality of training data fundamentally determines the capabilities of large language models (LLMs), yet no unified benchmark exists to measure how well LLMs, agents, and data-centric workflows actually prepare training data end to end.