Ranking Abuse via Strategic Pairwise Data Perturbations

• URL: http://arxiv.org/abs/2604.17805v1
• Date: Mon, 20 Apr 2026 04:52:30 GMT
• Title: Ranking Abuse via Strategic Pairwise Data Perturbations
• Abstract summary: We study the vulnerability of MLE-based ranking systems to adversarial perturbations.<n> Experimental results on both synthetic data and real-world election datasets show that MLE-based rankings exhibit a sharp phase-transition behavior.
• Score: 4.6840536642016195
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Pairwise ranking systems based on Maximum Likelihood Estimation (MLE), such as the Bradley-Terry model, are widely used to aggregate preferences from pairwise comparisons. However, their robustness under strategic data manipulation remains insufficiently understood. In this paper, we study the vulnerability of MLE-based ranking systems to adversarial perturbations. We formulate the manipulation task as a constrained combinatorial optimization problem and propose an Adaptive Subset Selection Attack (ASSA) to efficiently identify high-impact perturbations. Experimental results on both synthetic data and real-world election datasets show that MLE-based rankings exhibit a sharp phase-transition behavior: beyond a small perturbation budget, a limited number of strategic voters can significantly alter the global ranking. In particular, our method consistently outperforms random and greedy baselines under constrained budgets. These findings reveal a fundamental sensitivity of MLE-based ranking mechanisms to structured perturbations and highlight the need for more robust aggregation methods in collective decision-making systems.

Related papers

• Preference Data Selection for Mitigating the Alignment Tax in Large Language Models [52.18310687523229]
  BALIGN is a balanced data selection strategy that mitigates catastrophic forgetting while optimizing alignment efficacy.<n>By aggregating three key data-centric features into a unified composite risk score, BALIGN systematically filters out high-risk preference samples.<n>Experiments on standard human preference datasets demonstrate that BALIGN strongly preserves foundational capabilities without compromising alignment gains.
  arXiv  Detail & Related papers  (2026-08-25T07:58:42Z)
• A Unified Algorithmic Framework for Hybrid Reinforcement Learning in Tabular MDPs with Shifted Transition Dynamics [36.92549343895364]
  We propose a unified algorithmic framework featuring two algorithms: MIN-UCB-VI for regret minimization and MAX-LCB-VI for best policy identification.<n>We provide theoretical guarantees for our framework, including both instance-dependent and independent upper bounds on regret and sub-optimality gap.
  arXiv  Detail & Related papers  (2026-07-28T02:27:27Z)
• Nonparametric LLM Evaluation from Preference Data [86.96268870461472]
  We propose a nonparametric statistical framework, DMLEval, for comparing and ranking large language models (LLMs) from preference data.<n>Our framework provides practitioners with powerful, state-of-the-art methods for comparing or ranking LLMs.
  arXiv  Detail & Related papers  (2026-01-29T15:00:07Z)
• Uncertainty-Aware Dual-Ranking Strategy for Offline Data-Driven Multi-Objective Optimization [9.62861758248967]
  offline data-driven Multi-Objective Optimization Problems (MOPs) rely on limited data from simulations, experiments, or sensors.<n>We propose a simple yet novel dual-ranking strategy, working with a basic multi-objective evolutionary algorithm, NSGA-II.<n>With this dual-ranking strategy, each solution's final rank is the average of its non-dominated sorting rank and a rank derived from the uncertainty-adjusted fitness function.
  arXiv  Detail & Related papers  (2025-11-09T16:53:40Z)
• NDCG-Consistent Softmax Approximation with Accelerated Convergence [67.10365329542365]
  We propose novel loss formulations that align directly with ranking metrics.<n>We integrate the proposed RG losses with the highly efficient Alternating Least Squares (ALS) optimization method.<n> Empirical evaluations on real-world datasets demonstrate that our approach achieves comparable or superior ranking performance.
  arXiv  Detail & Related papers  (2025-06-11T06:59:17Z)
• In-context Ranking Preference Optimization [65.5489745857577]
  We propose an In-context Ranking Preference Optimization (IRPO) framework to optimize large language models (LLMs) based on ranking lists constructed during inference.<n>We show IRPO outperforms standard DPO approaches in ranking performance, highlighting its effectiveness in aligning LLMs with direct in-context ranking preferences.
  arXiv  Detail & Related papers  (2025-04-21T23:06:12Z)
• AdaptiveMDL-GenClust: A Robust Clustering Framework Integrating Normalized Mutual Information and Evolutionary Algorithms [0.0]
  We introduce a robust clustering framework that integrates the Minimum Description Length (MDL) principle with a genetic optimization algorithm.<n>The framework begins with an ensemble clustering approach to generate an initial clustering solution, which is refined using MDL-guided evaluation functions and optimized through a genetic algorithm.<n> Experimental results demonstrate that our approach consistently outperforms traditional clustering methods, yielding higher accuracy, improved stability, and reduced bias.
  arXiv  Detail & Related papers  (2024-11-26T20:26:14Z)
• SeRA: Self-Reviewing and Alignment of Large Language Models using Implicit Reward Margins [30.767203592231496]
  Self-Reviewing and Alignment (SeRA) is a cost-efficient and effective method that can be readily combined with existing DAAs. SeRA comprises of two components: (1) sample selection using implicit reward margins, which helps alleviate over-fitting to some undesired features, and (2) preference bootstrapping using implicit rewards to augment preference data with updated policy models.
  arXiv  Detail & Related papers  (2024-10-12T04:17:28Z)
• Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization [75.1240295759264]
  We propose an effective framework for Bridging and Modeling Correlations in pairwise data, named BMC.<n>We increase the consistency and informativeness of the pairwise preference signals through targeted modifications.<n>We identify that DPO alone is insufficient to model these correlations and capture nuanced variations.
  arXiv  Detail & Related papers  (2024-08-14T11:29:47Z)
• Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function [106.69643619725652]
  We develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results. We report state-of-the-art results for text classification task on several benchmark datasets.
  arXiv  Detail & Related papers  (2020-09-08T21:55:22Z)

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.