arXiv:2602.13626v3 Announce Type: replace 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 sc
The rapid deployment and integration of Large Language Models (LLMs) into critical applications like recommender systems necessitate robust evaluation, making benchmark integrity a pressing concern.
This research reveals a fundamental flaw in how LLMs' performance might be perceived in recommendation systems, potentially leading to over-reliance on misleading metrics and suboptimal algorithmic adoption.
The understanding of true LLM performance in recommendation systems changes, requiring new evaluation methodologies, more rigorous dataset management, and potentially a re-assessment of deployed models.
- · AI ethics researchers
- · Companies with proprietary, insulated datasets
- · New evaluation methodology providers
- · Makers of synthetic data
- · LLM developers relying on public benchmarks
- · Businesses adopting LLMs based on inflated performance claims
- · Users of recommendation systems receiving suboptimal suggestions
- · Public benchmark creators without robust leakage detection
Increased scrutiny and demand for new benchmarks for LLM evaluation in recommendation systems.
A shift towards more private or synthetically generated datasets for LLM fine-tuning and evaluation to mitigate leakage.
Potential erosion of trust in reported AI performance metrics across various domains, leading to more cautious adoption and stricter regulatory oversight.
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Read at arXiv cs.LG