The paper "Benchmark Data Contamination of Large Language Models: A Survey" by Cheng Xu, Shuhao Guan, Derek Greene, and M-Tahar Kechadi from University College Dublin, Ireland, addresses the significant issue of Benchmark Data Contamination (BDC) in the evaluation of Large Language Models (LLMs). BDC occurs when LLMs inadvertently incorporate evaluation benchmark information from their training data, leading to inaccurate or unreliable performance during the evaluation phase. The authors review the complex challenge of BDC, explore alternative assessment methods to mitigate traditional benchmark risks, and examine challenges and future directions in mitigating BDC risks. The paper is structured into several sections, including an introduction, background on LLMs and BDC, detection techniques, and mitigation strategies. It defines BDC and categorizes it into four levels: semantic, information, data, and label levels, with the severity increasing from semantic to label levels. The authors discuss the sources and impact of BDC, highlighting that it primarily stems from the diverse and extensive pre-training datasets used in LLMs. In the detection techniques section, the paper reviews matching-based and comparison-based methods. Matching-based methods focus on examining overlaps and inclusions in pre-training data and evaluation datasets, while comparison-based methods involve comparing model performance on evaluation datasets. The paper also discusses the limitations of these methods, such as computational demands and the potential for evasive techniques to bypass detection. The mitigation strategies section categorizes approaches into curating new data, refactoring existing data, and benchmark-free evaluation. Curating new data involves using private or dynamic benchmarks to isolate evaluation data from pre-training datasets. Refactoring existing data includes techniques like data regeneration and content filtering to enhance evaluation reliability. Benchmark-free evaluation aims to avoid relying on predefined benchmarks altogether. Overall, the paper provides a comprehensive survey of BDC in LLMs, offering insights into detection and mitigation strategies to ensure the reliability and validity of LLM evaluations.The paper "Benchmark Data Contamination of Large Language Models: A Survey" by Cheng Xu, Shuhao Guan, Derek Greene, and M-Tahar Kechadi from University College Dublin, Ireland, addresses the significant issue of Benchmark Data Contamination (BDC) in the evaluation of Large Language Models (LLMs). BDC occurs when LLMs inadvertently incorporate evaluation benchmark information from their training data, leading to inaccurate or unreliable performance during the evaluation phase. The authors review the complex challenge of BDC, explore alternative assessment methods to mitigate traditional benchmark risks, and examine challenges and future directions in mitigating BDC risks. The paper is structured into several sections, including an introduction, background on LLMs and BDC, detection techniques, and mitigation strategies. It defines BDC and categorizes it into four levels: semantic, information, data, and label levels, with the severity increasing from semantic to label levels. The authors discuss the sources and impact of BDC, highlighting that it primarily stems from the diverse and extensive pre-training datasets used in LLMs. In the detection techniques section, the paper reviews matching-based and comparison-based methods. Matching-based methods focus on examining overlaps and inclusions in pre-training data and evaluation datasets, while comparison-based methods involve comparing model performance on evaluation datasets. The paper also discusses the limitations of these methods, such as computational demands and the potential for evasive techniques to bypass detection. The mitigation strategies section categorizes approaches into curating new data, refactoring existing data, and benchmark-free evaluation. Curating new data involves using private or dynamic benchmarks to isolate evaluation data from pre-training datasets. Refactoring existing data includes techniques like data regeneration and content filtering to enhance evaluation reliability. Benchmark-free evaluation aims to avoid relying on predefined benchmarks altogether. Overall, the paper provides a comprehensive survey of BDC in LLMs, offering insights into detection and mitigation strategies to ensure the reliability and validity of LLM evaluations.