Proper Dataset Valuation by Pointwise Mutual Information
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2603. 19294v4 Announce Type: replace Abstract: While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labeled data or external verifiers.
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.
The paper introduces prediction‑powered smoothing (PP‑S) and its taxonomy‑aware extension (PP‑TS) to improve point and interval estimates of domain‑specific AI performance when only a limited sample of labeled units is available. It also proposes a new design‑based cross‑validation score that is approximately unbiased for selecting between direct and smoothed estimators. Experiments on a curated benchmark and real‑world agent traffic show that the proposed methods outperform direct estimators in both accuracy and coverage, and that the new score matches the performance of an independent validation sample while providing more precise error estimates.
The paper introduces a framework for hypothesis testing that combines inexpensive AI judgments with selective human verification to control type‑I and type‑II errors while minimizing cost. It derives an information‑theoretic lower bound on the minimum cost and proposes the SCALE policy, a sequential, cost‑aware strategy that adapts AI scoring and human escalation. SCALE is proven valid for finite samples and asymptotically matches the lower bound, achieving significant savings when both AI and human inputs are valuable.
arXiv:2607. 16239v1 Announce Type: new Abstract: AI judges offer a scalable, low-cost alternative to human evaluation, but their outputs can be biased relative to human preferences and highly item-dependent, varying across judges, tasks, and domains.
The paper introduces Debiased Inference with Multiple Imperfect Measurements (DMM), a framework that uses several error‑prone AI measurements to perform valid downstream statistical inference without requiring costly gold‑standard labels. By assuming conditional independence of the measurements given the true label and unit‑level features, DMM leverages CP decomposition and semiparametric theory to prove consistency and asymptotic normality of its estimator. Simulations demonstrate that DMM yields valid inference and can improve efficiency when additional imperfect measurements are available, and the authors provide diagnostics for the key independence assumption.