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[2406.04244] Benchmark Data Contamination of Large Language Models: A Survey

arxiv.org · 7,179 words · saved by 1 readers

Abstract: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.

Benchmark Data Contamination of Large Language Models: A Survey CHENG XU, University College Dublin, Ireland SHUHAO GUAN, University College Dublin, Ireland arXiv:2406.04244v1 [cs.CL] 6 Jun 2024 DEREK GREENE, University College Dublin, Ireland M-TAHAR KECHADI, University College Dublin, Ireland The rapid development of Large Language Models (LLMs) like…

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