Benchmark Leakage Trap: Can We Trust LLM-based Recommendation?

• URL: http://arxiv.org/abs/2602.13626v1
• Date: Sat, 14 Feb 2026 06:34:19 GMT
• Title: Benchmark Leakage Trap: Can We Trust LLM-based Recommendation?
• Authors: Mingqiao Zhang, Qiyao Peng, Yumeng Wang, Chunyuan Liu, Hongtao Liu,
• Abstract summary: This paper identifies and investigates a previously overlooked issue: benchmark data leakage in LLM-based recommendation.<n>Data leakage acts as a critical, previously unaccounted-for factor in LLM-based recommendation, which could impact the true model performance.
• Score: 9.574427977779235
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The expanding integration of Large Language Models (LLMs) into recommender systems poses critical challenges to evaluation reliability. This paper identifies and investigates a previously overlooked issue: benchmark data leakage in LLM-based recommendation. This phenomenon occurs when LLMs are exposed to and potentially memorize benchmark datasets during pre-training or fine-tuning, leading to artificially inflated performance metrics that fail to reflect true model performance. To validate this phenomenon, we simulate diverse data leakage scenarios by conducting continued pre-training of foundation models on strategically blended corpora, which include user-item interactions from both in-domain and out-of-domain sources. Our experiments reveal a dual-effect of data leakage: when the leaked data is domain-relevant, it induces substantial but spurious performance gains, misleadingly exaggerating the model's capability. In contrast, domain-irrelevant leakage typically degrades recommendation accuracy, highlighting the complex and contingent nature of this contamination. Our findings reveal that data leakage acts as a critical, previously unaccounted-for factor in LLM-based recommendation, which could impact the true model performance. We release our code at https://github.com/yusba1/LLMRec-Data-Leakage.

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