Pairwise Reference Alignment as a Model-Level Ordinal Observable

• URL: http://arxiv.org/abs/2605.30758v1
• Date: Fri, 29 May 2026 02:41:18 GMT
• Title: Pairwise Reference Alignment as a Model-Level Ordinal Observable
• Abstract summary: Given a reference distribution of pairwise preferences, what model-level quantity is estimated when we test whether a model ranks preferred responses above rejected responses?<n>We define pairwise reference alignment as an ordinal observable induced by a model scoring function.<n>The resulting quantities admit simple finite-sample estimators and concentration bounds under independent sampling assumptions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pairwise preference data is widely used in language-model evaluation and alignment, often for model ranking, reward modeling, or preference optimization. This note formulates a more basic measurement question: given a reference distribution of pairwise preferences, what model-level quantity is estimated when we test whether a model ranks preferred responses above rejected responses? We define pairwise reference alignment as an ordinal observable induced by a model scoring function. Given a reference pair distribution $P_{\mathrm{pair}}$ over triples $(x,y^+,y^-)$, and a scalar model score $S_M(x,y)$, we define the alignment observable as the probability that the model-induced ordering agrees with the reference preference ordering. We further define a centered order-parameter-like statistic and discuss a margin-based extension. The resulting quantities admit simple finite-sample estimators and concentration bounds under independent sampling assumptions. This note does not introduce a new benchmark. It provides a conceptual and statistical formulation for pairwise reference alignment, clarifies the role of the reference pair distribution, and distinguishes the general ordinal observable from scoring choices such as normalized log-probability or energy-based scores. We also provide an initial empirical study on Qwen2.5 models and RewardBench, where the proposed statistics increase with model size and instruction tuning and vary across reference-pair subsets as predicted by the formulation.

Related papers

• Generalized Score Matching for Parameter Estimation on Convex Domains [13.954816561132942]
  We derive the generalized score matching objective on a convex subset of $mathbbRd$.<n>We show how classical score matching as well as domain-adapted variants for non-negative data arise naturally within the proposed framework.<n>Our derivation sheds new light on the scope and applicability of generalized score matching in various problem settings.
  arXiv  Detail & Related papers  (2026-09-10T13:23:41Z)
• MBD: A Model-Based Debiasing Framework Across User, Content, and Model Dimensions [50.00784452900918]
  We propose a general, model-based debiasing (MBD) framework that addresses this challenge.<n>We explicitly estimate the contextual mean and variance of the engagement distribution for arbitrary cohorts.<n>This integration allows the framework to convert biased raw signals into unbiased representations.
  arXiv  Detail & Related papers  (2026-03-15T15:07:01Z)
• Model Correlation Detection via Random Selection Probing [62.093777777813756]
  Existing similarity-based methods require access to model parameters or produce scores without thresholds.<n>We introduce Random Selection Probing (RSP), a hypothesis-testing framework that formulates model correlation detection as a statistical test.<n>RSP produces rigorous p-values that quantify evidence of correlation.
  arXiv  Detail & Related papers  (2025-09-29T01:40:26Z)
• Learning Parametric Distributions from Samples and Preferences [19.879505582147807]
  We show that preference-based M-estimators achieve a better variance than sample-only M-estimators.<n>We propose an estimator achieving an estimation error scaling of $mathcalO (1/n)$ -- a significant improvement over the $Theta (1/sqrtn)$ rate attainable with samples alone.
  arXiv  Detail & Related papers  (2025-05-29T15:33:43Z)
• Model-free Methods for Event History Analysis and Efficient Adjustment (PhD Thesis) [55.2480439325792]
  This thesis is a series of independent contributions to statistics unified by a model-free perspective.<n>The first chapter elaborates on how a model-free perspective can be used to formulate flexible methods that leverage prediction techniques from machine learning.<n>The second chapter studies the concept of local independence, which describes whether the evolution of one process is directly influenced by another.
  arXiv  Detail & Related papers  (2025-02-11T19:24:09Z)
• Robust Ordinal Regression for Subsets Comparisons with Interactions [2.6151761714896122]
  This paper is dedicated to a robust ordinal method for learning the preferences of a decision maker between subsets. The decision model, derived from Fishburn and LaValle, is general enough to be compatible with any strict weak order on subsets. A predicted preference is considered reliable if all the simplest models (Occam's razor) explaining the preference data agree on it.
  arXiv  Detail & Related papers  (2023-08-07T07:54:33Z)
• Score-based Continuous-time Discrete Diffusion Models [102.65769839899315]
  We extend diffusion models to discrete variables by introducing a Markov jump process where the reverse process denoises via a continuous-time Markov chain. We show that an unbiased estimator can be obtained via simple matching the conditional marginal distributions. We demonstrate the effectiveness of the proposed method on a set of synthetic and real-world music and image benchmarks.
  arXiv  Detail & Related papers  (2022-11-30T05:33:29Z)
• Optimizing model-agnostic Random Subspace ensembles [5.680512932725364]
  We present a model-agnostic ensemble approach for supervised learning. The proposed approach alternates between learning an ensemble of models using a parametric version of the Random Subspace approach. We show the good performance of the proposed approach, both in terms of prediction and feature ranking, on simulated and real-world datasets.
  arXiv  Detail & Related papers  (2021-09-07T13:58:23Z)
• A Normative Model of Classifier Fusion [4.111899441919164]
  We present a hierarchical Bayesian model of probabilistic classification fusion based on a new correlated Dirichlet distribution. The proposed model naturally accommodates the classic Independent Opinion Pool and other independent fusion algorithms as special cases. It is evaluated by uncertainty reduction and correctness of fusion on synthetic and real-world data sets.
  arXiv  Detail & Related papers  (2021-06-03T11:52:13Z)
• Nonparametric Score Estimators [49.42469547970041]
  Estimating the score from a set of samples generated by an unknown distribution is a fundamental task in inference and learning of probabilistic models. We provide a unifying view of these estimators under the framework of regularized nonparametric regression. We propose score estimators based on iterative regularization that enjoy computational benefits from curl-free kernels and fast convergence.
  arXiv  Detail & Related papers  (2020-05-20T15:01:03Z)
• Decision-Making with Auto-Encoding Variational Bayes [71.44735417472043]
  We show that a posterior approximation distinct from the variational distribution should be used for making decisions. Motivated by these theoretical results, we propose learning several approximate proposals for the best model. In addition to toy examples, we present a full-fledged case study of single-cell RNA sequencing.
  arXiv  Detail & Related papers  (2020-02-17T19:23:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.