  The rapid development of Large Language Models (LLMs) like GPT-4, Claude-3,
and Gemini has transformed the field of natural language processing. However,
it has also resulted in a significant issue known as Benchmark Data
Contamination (BDC). This occurs when language models inadvertently incorporate
evaluation benchmark information from their training data, leading to
inaccurate or unreliable performance during the evaluation phase of the
process. This paper reviews the complex challenge of BDC in LLM evaluation and
explores alternative assessment methods to mitigate the risks associated with
traditional benchmarks. The paper also examines challenges and future
directions in mitigating BDC risks, highlighting the complexity of the issue
and the need for innovative solutions to ensure the reliability of LLM
evaluation in real-world applications.
