ELSPR: Evaluator LLM Training Data Self-Purification on Non-Transitive Preferences via Tournament Graph Reconstruction

• URL: http://arxiv.org/abs/2505.17691v2
• Date: Tue, 26 Aug 2025 03:56:22 GMT
• Title: ELSPR: Evaluator LLM Training Data Self-Purification on Non-Transitive Preferences via Tournament Graph Reconstruction
• Authors: Yan Yu, Yilun Liu, Minggui He, Shimin Tao, Weibin Meng, Xinhua Yang, Li Zhang, Hongxia Ma, Dengye Li, Daimeng Wei, Boxing Chen, Fuliang Li,
• Abstract summary: Pairwise evaluation of large language models (LLMs) has become the dominant paradigm for benchmarking open-ended tasks.<n>We show that this critical issue stems largely from low-quality data that contains inherently ambiguous preference pairs.<n>We propose ELSPR, a principled graph-theoretic framework that models pairwise preferences as tournament graphs.
• Score: 25.85736569130897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pairwise evaluation of large language models (LLMs) has become the dominant paradigm for benchmarking open-ended tasks, yet non-transitive preferences, where evaluators prefer A over B, B over C, but C over A, fundamentally undermine ranking reliability. We show that this critical issue stems largely from low-quality data that contains inherently ambiguous preference pairs. To address this challenge, we propose ELSPR, a principled graph-theoretic framework that models pairwise preferences as tournament graphs and systematically identifies problematic training data. ELSPR quantifies non-transitivity through strongly connected components (SCCs) analysis and measures overall preference clarity using a novel normalized directed graph structural entropy metric. Our filtering methodology selectively removes preference data that induce non-transitivity while preserving transitive preferences. Extensive experiments on the AlpacaEval benchmark demonstrate that models fine-tuned on ELSPR-filtered data achieve substantial improvements: a 13.8% reduction in non-transitivity, a 0.088 decrease in structural entropy, and significantly enhanced discriminative power in real-world evaluation systems. Human validation confirms that discarded data exhibit dramatically lower inter-annotator agreement (34.4% vs. 52.6%) and model-human consistency (51.2% vs. 80.6%) compared to cleaned data. These findings establish ELSPR as an effective data self-purification approach for developing more robust, consistent, and human-aligned LLM evaluation systems.

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